{"as_of":"2026-08-10T13:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bc26417fe55e5b69ab94299dbc01f988a9850974aa01aad44572577449e271a1","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:29:11.031879Z","state":"measured"},{"denominator":67,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":67,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.15709/citation-record","integrity":"/paper/2507.15709/integrity","json":"/paper/2507.15709/citation-record.json","paper":"/paper/2507.15709"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:21.329271Z","title":"Arniqa: Learning distortion mani- fold for image quality assessment","venue":null,"work_id":"bedf5888-5eb4-4f47-bd3f-7df622efeae4","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.225121Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b614a2eb8c645a085b0b441964efc782a61cf6079ba47eb4040a9580533352d4","observation_id":"aeff3275-bc61-4bff-9f00-5311791fc8ff","resolution":{"observed_at":"2026-08-06T15:29:21.395856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:21.140410Z","title":"A fast approach for no- reference image sharpness assessment based on maximum local variation","venue":null,"work_id":"33169072-328f-42f9-9739-86a4a3f0b8ae","year":2014},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.296009Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b43a1402f18d4c8e8083656f4aab486519af61018fc319d0924c25eda69f9bac","observation_id":"04862b7f-f8bc-4c93-a900-4ac3fe3f9c2b","resolution":{"observed_at":"2026-08-06T15:29:21.246679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:20.988171Z","title":"Deep neural net- works for no-reference and full-reference image quality as- sessment","venue":null,"work_id":"9753df05-c13f-4981-ad7c-1ae99da03d4e","year":2017},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.339754Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:99932f00b029af5b3875e0ffed40290f9540f7098fc8774f2ee9baf81cabdaea","observation_id":"a2757005-d808-453f-a770-ff70b15db843","resolution":{"observed_at":"2026-08-06T15:29:21.042022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03631","last_updated":"2026-05-24T07:16:25Z","snapshot_observed_at":"2026-08-07T15:49:28.795156Z","submitted_at":"2025-05-06T15:29:32Z","title":"Generalizable Video Quality Assessment via Weak-to-Strong Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.03631","snapshot_observed_at":"2026-08-06T15:29:05.415710Z","title":"Breaking annotation barriers: Generalized video quality assessment via ranking-based self- supervision","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.415710Z"},"links":{"cited_paper":"/paper/2505.03631","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:c49daa1219f218d6366f5ee2d7c1217a2f9746dfef3138fbe9193d3b2f5f8f6c","observation_id":"2584eb19-2379-4360-beae-89ead8fbb5e7","resolution":{"observed_at":"2026-08-06T15:29:05.415710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:20.891783Z","title":"Attention-guided neural networks for full-reference and no- reference audio-visual quality assessment","venue":null,"work_id":"d88d1ae6-ca2f-43ad-9769-1359cf01ee10","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.493635Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:cd20600e4b6cb6ffcf9eafbc7a3ca8a8aed1bcc9fc2baea7d11aa503c5969021","observation_id":"ce0c56af-945e-47fb-9021-dd4731924174","resolution":{"observed_at":"2026-08-06T15:29:20.969355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18314","last_updated":"2025-07-14T07:08:07Z","snapshot_observed_at":"2026-08-09T23:52:58.437414Z","submitted_at":"2025-01-30T12:43:47Z","title":"AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.18314","snapshot_observed_at":"2026-08-06T15:29:05.574108Z","title":"Agav-rater: adapting large multimodal model for ai-generated audio-visual quality assessment","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.574108Z"},"links":{"cited_paper":"/paper/2501.18314","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:96789f77d0cf955106ecda794b993d501b2c689edd34378a4bf2e0d9f716f67d","observation_id":"13e43b0e-11d7-4144-a940-7bd6e6e57ace","resolution":{"observed_at":"2026-08-06T15:29:05.574108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:20.692229Z","title":"An image quality assessment dataset for portraits","venue":null,"work_id":"d7fc1ce5-ab4f-41b3-9422-058af484e03f","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.656302Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:dfe597bc82cad6be3055af1c4456a84b5a46d5704083e2ddadd27a28c31916a1","observation_id":"5df396e3-902d-4a27-a36e-618d60a77f4f","resolution":{"observed_at":"2026-08-06T15:29:20.786444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:20.455579Z","title":"Topiq: A top-down approach from semantics to distortions for image quality assessment","venue":null,"work_id":"f04751c6-c0d8-4f90-a77e-2a8532261ef9","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.729170Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:7dc8f583432555a06ad6572337323440b51c1735a6e6da1eab81cd5409c78c4e","observation_id":"01fdc216-9530-41d3-88e4-57065d1cb31d","resolution":{"observed_at":"2026-08-06T15:29:20.566986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:20.279284Z","title":"Dsl-fiqa: As- sessing facial image quality via dual-set degradation learn- ing and landmark-guided transformer","venue":null,"work_id":"2569658f-ec31-43f0-9d08-723468a8a0e6","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.813232Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:08c4822d7373305acf4b7dab4d98b82def2ccb9eb1e14d13ba4422eaba2e96b8","observation_id":"8bc1f2f1-a067-44d6-ab16-993a607d5237","resolution":{"observed_at":"2026-08-06T15:29:20.333596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:05.824444Z","title":"Vquala 2025 chal- lenge on genai-bench aigc video quality assessment: Meth- ods and results","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.824444Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:c37cbb36272d632ea1a9c854f36372d6ea31406018949b44804be9ecb00067c5","observation_id":"ef3ff400-c691-4949-baa1-4ca7ff666f75","resolution":{"observed_at":"2026-08-06T15:29:05.824444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:20.130935Z","title":"A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb)","venue":null,"work_id":"c2d4d371-8225-4208-9845-de8d86b76071","year":2009},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:05.937552Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:a14b3902a095661b63fb1557690c8f9edc6ab113cadb2e53b410e4b4560a5854","observation_id":"4f2c58f3-c431-48d6-b35b-e508b1c286d8","resolution":{"observed_at":"2026-08-06T15:29:20.208425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:19.926729Z","title":"Born again neural net- works","venue":null,"work_id":"59f4efd8-ccb7-4152-9cdb-8f6a13034810","year":2018},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:06.093278Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:837d2562f6810e392bdb62c745f7c177089e57571ad228d3afdb47a17a67dfcd","observation_id":"632722d1-1bf0-491c-bd6c-371550dd3b8a","resolution":{"observed_at":"2026-08-06T15:29:20.023467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:19.720456Z","title":"Lmm-vqa: Advancing video quality assessment with large multimodal models.IEEE Transactions on Circuits and Systems for Video Technology, 2025","venue":null,"work_id":"27b8e9e9-5c3a-4ab8-ac3e-8d1f9172a85f","year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:06.208570Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:2efcf5e89d04d502bdab50edab0387eee4ae38aa2cf4412956b4ca60256e0eaa","observation_id":"5108c18d-6417-48b4-b8a6-59db0e500c70","resolution":{"observed_at":"2026-08-06T15:29:19.798822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:19.612693Z","title":"Faceqnet: Quality assessment for face recognition based on deep learning","venue":null,"work_id":"164fc7a7-b7b5-4653-8bd1-a1c64d9b3bd4","year":null},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:06.333915Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:6a359176465912c31b7b26a53a5ec172ee67b08c457ad0799b599a0df099ed02","observation_id":"58e63c1e-71ab-4d61-be19-2a6d19e51ada","resolution":{"observed_at":"2026-08-06T15:29:19.702697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T15:29:06.465845Z","title":"Distill- ing the knowledge in a neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:06.465845Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:f404f03a1f0690eb13fb8147fc5f205fab9725458de6ca912cbe972f208b56eb","observation_id":"37ea31b2-0831-4f4d-b996-c171ba633e84","resolution":{"observed_at":"2026-08-06T15:29:06.465845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:06.589769Z","title":"Vquala 2025 doc- ument image quality assessment challenge","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:06.589769Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:5aea1c31d476eca46f3bc71b9af4fdcfe8d38c8899ff6071e6507d08ab9ea1b2","observation_id":"3037ad50-660a-407b-97c0-3ef663b1e8d1","resolution":{"observed_at":"2026-08-06T15:29:06.589769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:19.389231Z","title":"Convolu- tional neural networks for no-reference image quality assess- ment","venue":null,"work_id":"b27288aa-ebe7-49ee-83a3-7b7b30ba928b","year":2014},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:06.933959Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:f9f5ed7d69aba1d06e5d64fd8a2a4ae161d8107deda1bc7d702febbddd60c493","observation_id":"d00f219a-1d77-4753-b5ad-abb71b552800","resolution":{"observed_at":"2026-08-06T15:29:19.503211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:19.124512Z","title":"Musiq: Multi-scale image quality transformer","venue":null,"work_id":"49e02fb7-f446-41f3-9902-d173b2915bd6","year":2021},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.199544Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:cd380c161cf5d5357b6555e11906f3add9b0be40759e58df5fadd16ce0ce89ea","observation_id":"3169d004-b109-473f-af5c-a017d91829d3","resolution":{"observed_at":"2026-08-06T15:29:19.226080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:07.400865Z","title":"Vquala 2025 challenge on engagement prediction for short videos: Methods and results","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.400865Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b848c95ab55601061514fec80162a46ad2ece6f6aa9689f0a698097d4c871bd8","observation_id":"2fae1ce2-16a9-4396-adbc-c3e227bca5a3","resolution":{"observed_at":"2026-08-06T15:29:07.400865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:07.556017Z","title":"Vquala 2025 challenge on image super-resolution generated content qual- ity assessment: Methods and results","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.556017Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:bb0e6e50c4b79cc33c0616accfbab6292991491330b23dfbeaf6b0b29bd40271","observation_id":"cfa25032-0bd5-4003-911c-09708b0dd3d1","resolution":{"observed_at":"2026-08-06T15:29:07.556017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21308","last_updated":"2025-04-30T04:36:56Z","snapshot_observed_at":"2026-08-07T15:57:55.284768Z","submitted_at":"2025-04-30T04:36:56Z","title":"AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21308","snapshot_observed_at":"2026-08-06T15:29:07.699638Z","title":"Aghi-qa: A subjective-aligned dataset and metric for ai-generated human images","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.699638Z"},"links":{"cited_paper":"/paper/2504.21308","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:fe8e26586995792c5c74781b8bfc28d1ee1d8b6df24413a9a6ae204b5482ef7c","observation_id":"c4bf723c-b1ec-4d81-a73a-ac9f423efd43","resolution":{"observed_at":"2026-08-06T15:29:07.699638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:18.856128Z","title":"Assessing face image quality: A large-scale database and a transformer method","venue":null,"work_id":"f6d39f01-e3b3-4b03-b0a5-23d9050290a0","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.827876Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:66e3453c68911d2378e0ad2a3128f45ac030cc8e32262a4f004a670cbe95b70c","observation_id":"ceeb3fa2-d187-40b2-8338-776b88beb775","resolution":{"observed_at":"2026-08-06T15:29:18.960620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:18.568431Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"f3e09eb1-afdf-4f8e-b303-3342b2f340bd","year":2021},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.861860Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:eab939cbd68fbf49b899f5ea1d2b005e49c6ceb97cc1da2e6a5a50e0cd5d5840","observation_id":"48a12e14-0365-49d4-8e68-caad9edb9bf5","resolution":{"observed_at":"2026-08-06T15:29:18.719385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:18.297235Z","title":"Bh-vqa: blind high frame rate video quality assessment","venue":null,"work_id":"ce4f205a-557f-4673-8a9a-c2bba837f888","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.914661Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:3e1fcea6a320ddf4ea80cbb3925ac94e821d7604144d09d3b343b5f4055d82bd","observation_id":"7139c027-37c8-4b90-8f71-b687ff557642","resolution":{"observed_at":"2026-08-06T15:29:18.403972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:07.988302Z","title":"Vquala 2025 challenge on face image quality assessment: Methods and results","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:07.988302Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:bf36809a4706885c79d866655c1ba571133017920886e3aef1b402b13b559d60","observation_id":"572f399e-c094-4680-9a42-95e2193527a1","resolution":{"observed_at":"2026-08-06T15:29:07.988302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:18.004606Z","title":"Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications","venue":null,"work_id":"2166cf05-7b6a-47f3-81af-3c2a1cbb84fb","year":2022},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.038219Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:1f27285f78e01f191850b5146f7d54fd113f2665db1e16a8442652e3091b5849","observation_id":"650c1046-24c3-4aa1-968e-6b24ed674ba1","resolution":{"observed_at":"2026-08-06T15:29:18.149377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:17.710987Z","title":"Image quality assessment us- ing contrastive learning","venue":null,"work_id":"ccda4a48-9cd5-4031-8e4d-e1744ec746fe","year":2022},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.116874Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:f34331904c0bffbefa3f64d95e0c8ffd9b6eb5eb0a681d8102394c81dd32e672","observation_id":"ec205cf4-c4d4-4c71-860b-7820e321f2f5","resolution":{"observed_at":"2026-08-06T15:29:17.879171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:17.373610Z","title":"No-reference image quality assessment in the spa- tial domain","venue":null,"work_id":"8500ee84-5864-4ac7-8ccd-322757fe314f","year":2012},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.143280Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:ce9197cf2853eab186be62eccd66d7f6cb2ccac68d1118a042fe780e6a95164b","observation_id":"48b3afa2-3039-4d51-bcf7-d5b632e97a20","resolution":{"observed_at":"2026-08-06T15:29:17.549246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:17.108841Z","title":"completely blind","venue":null,"work_id":"912984c5-e758-4ad0-8044-4ff4798a0511","year":2012},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.178789Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:73a1a740119cca7df854caebb43f164e4464444bc734d5095379d73a57b7cf60","observation_id":"aa2d7293-c0b8-4bba-b8aa-12a86f74e9f3","resolution":{"observed_at":"2026-08-06T15:29:17.214136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:16.818619Z","title":"Blind im- age quality assessment: From natural scene statistics to per- ceptual quality","venue":null,"work_id":"bd816c4c-70fb-40b1-b5ac-a1a0cd7b3d09","year":2011},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.233381Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:054c00b5de38e8ed4e553ab337428145f1a3751ca5eb9d42bad77a8886309cf3","observation_id":"4ba447a3-b3ec-4a00-afa2-b54a1480722a","resolution":{"observed_at":"2026-08-06T15:29:16.969209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:16.587254Z","title":"A no-reference im- age blur metric based on the cumulative probability of blur detection (cpbd)","venue":null,"work_id":"b441f4ed-db38-4313-b98e-bd70f2f6df51","year":2011},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.285593Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:e9e2cc2f2dad58eec659e43590cfac4ff61fcdae8e453e24d2e89aff247ff750","observation_id":"0b8c3bde-7316-4e99-badc-79a6f0a0cba0","resolution":{"observed_at":"2026-08-06T15:29:16.697043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:16.374672Z","title":"Sdd-fiqa: unsupervised face image quality assess- ment with similarity distribution distance","venue":null,"work_id":"3597116f-f9a0-4309-a3b3-0099c7069ee0","year":2021},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.350391Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:541c6996984a915efe2a2938f6b7885fa310192b035652e19f9877fe4b3d3524","observation_id":"a0a56354-913b-4970-ac5a-8b02a14de018","resolution":{"observed_at":"2026-08-06T15:29:16.461915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:16.208971Z","title":"Statistics of natural images: Scaling in the woods","venue":null,"work_id":"024e3d75-d168-42cf-8148-61eada164745","year":1994},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.418777Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:18be3ca405edb916d2bf80fc90850bdbb9eb3bf5d84ed3dafa0d2626da16e37f","observation_id":"88b08012-d188-457e-b6c7-11c761240a31","resolution":{"observed_at":"2026-08-06T15:29:16.275842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.994849Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":"b6914581-7bcb-46db-997c-4f000651e78d","year":2018},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.478411Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b8241ad5ac935a2676f7dfd9021f7c128b1bef7f5cf21b3e03fefdd4e9207974","observation_id":"dcbd762a-42b3-48d4-a883-6d9d481325ad","resolution":{"observed_at":"2026-08-06T15:29:16.096036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.844823Z","title":"Face image quality assessment: A literature survey","venue":null,"work_id":"1121a527-c073-4e71-bd57-9aaf0489bcd3","year":2022},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.570391Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:2c5aa83d134387c3443028943cc73b77c3d2867e70a62c9f8984b62163f6f381","observation_id":"3b0fa87c-7edb-43ac-96fe-0a119914fbf0","resolution":{"observed_at":"2026-08-06T15:29:15.919572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1205.4831","last_updated":"2012-05-22T08:00:45Z","snapshot_observed_at":"2026-07-06T02:48:31.745141Z","submitted_at":"2012-05-22T08:00:45Z","title":"Gray Level Co-Occurrence Matrices: Generalisation and Some New Features","version":1},"cited_work":{"arxiv_id":"1205.4831","doi":null,"metadata_source":"pith","pith_arxiv_id":"1205.4831","snapshot_observed_at":"2026-08-06T15:29:11.494480Z","title":"Gray Level Co-Occurrence Matrices: Generalisation and Some New Features","venue":"cs.CV","work_id":"1af9e0fd-eeef-4c9c-8940-1881568b74df","year":2012},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.669011Z"},"links":{"cited_paper":"/paper/1205.4831","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:3dcb567c2f825f1a7f7c60dcca8f8931fc59d70af123c3f2ad30274c6d2b657e","observation_id":"6f2d5402-d0e5-43a1-8454-e309637c743f","resolution":{"observed_at":"2026-08-06T15:29:11.608763Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.663666Z","title":"Going the extra mile in face image quality assess- ment: A novel database and model","venue":null,"work_id":"20779d81-b1e1-4931-b57b-9d63cd74b31f","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.713072Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:7784d0273408259c79a022f6560cf0f35250a40688ab8d753cb1bc9972a8b39d","observation_id":"0fd3eecf-0ca7-4239-bf5c-ca59bb5fc2c5","resolution":{"observed_at":"2026-08-06T15:29:15.736227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.517304Z","title":"Mc360iqa: A multi-channel cnn for blind 360-degree image quality assessment","venue":null,"work_id":"f44ca799-6449-499e-a18f-3cb93f86f91c","year":2019},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.812327Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:9ffa7709c1718a15fb747fed02b0f46334f85b9f1401f6798208458b1bd2a43c","observation_id":"353b34c4-8112-4b8e-b4b9-1b7aa6b9f34f","resolution":{"observed_at":"2026-08-06T15:29:15.585223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.374424Z","title":"Deep learning based full-reference and no-reference quality assessment models for compressed ugc videos","venue":null,"work_id":"cceffe50-9a85-4fd0-812c-1a9c6f589697","year":2021},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.871381Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:2e8168fbc901dc9a8eb6dd3a6c90fac375d97e9e393a3ba7218bb27a10f7afdd","observation_id":"7b765838-f9f7-4cb4-8dd9-39832328b4cd","resolution":{"observed_at":"2026-08-06T15:29:15.431951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:08.944653Z","title":"A deep learning based no-reference quality assessment model for ugc videos","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:08.944653Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:94eb2d8c716537dc3c28e96a1830e55030f9ef866373eea03dec5b5dae4d65f7","observation_id":"c5a14cbe-355d-49be-81d5-8cd44fc9ca44","resolution":{"observed_at":"2026-08-06T15:29:08.944653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.184570Z","title":"Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training","venue":null,"work_id":"aeb4188c-911c-4fab-9946-9a6e5d1f169b","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.011508Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:21904a0fd4aa766642d8a00844dce0c1361e0eb0ebafecf38c6f683a7a72febe","observation_id":"0f979179-230f-4a46-bcd7-05e79b3c6651","resolution":{"observed_at":"2026-08-06T15:29:15.264157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:09.072718Z","title":"Enhancing blind video quality as- sessment with rich quality-aware features","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.072718Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:60b70c67042ab1ba8fefb061fcdca13e5328ddfb08bca0fa75179a313739e4f9","observation_id":"7c323b06-ed24-49da-bff9-01eaf2d4ab17","resolution":{"observed_at":"2026-08-06T15:29:09.072718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:15.021566Z","title":"Assessing uhd image quality from aesthetics, distor- tions, and saliency","venue":null,"work_id":"e232309a-ad4f-49c8-8933-4e5202946606","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.141008Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:bc4342c1f31538c3f977be170359ef0b577ebc01f3d49fa3ca070d9fe53e143c","observation_id":"e08d2744-0802-4d99-ad27-fb7cdd18a14c","resolution":{"observed_at":"2026-08-06T15:29:15.087481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.08555","last_updated":"2024-05-14T12:43:43Z","snapshot_observed_at":"2026-07-06T18:14:08.102261Z","submitted_at":"2024-05-14T12:43:43Z","title":"Dual-Branch Network for Portrait Image Quality Assessment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.08555","snapshot_observed_at":"2026-08-06T15:29:09.208893Z","title":"Dual-branch network for portrait image quality assessment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.208893Z"},"links":{"cited_paper":"/paper/2405.08555","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:372bb1a109fb6049be98013416d4f401bc08f5754cc13eb7f9d6c7154ac9de53","observation_id":"7423cacf-f57c-4f6d-a67f-627eff2459d5","resolution":{"observed_at":"2026-08-06T15:29:09.208893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.22790","last_updated":"2025-07-15T23:50:11Z","snapshot_observed_at":"2026-08-06T21:55:22.042119Z","submitted_at":"2025-06-28T07:14:23Z","title":"ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.22790","snapshot_observed_at":"2026-08-06T15:29:09.284389Z","title":"Compressedvqa-hdr: Generalized full-reference and no- reference quality assessment models for compressed high dynamic range videos","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.284389Z"},"links":{"cited_paper":"/paper/2506.22790","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:5e70d672dbbbbffeb624a46355982808babd06bd0f5108e35c8c43e89a6c45ee","observation_id":"28b78869-cd6d-4687-a215-d6016068f751","resolution":{"observed_at":"2026-08-06T15:29:09.284389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:09.380552Z","title":"An empirical study for efficient video quality assessment","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.380552Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:c6a101285b18cfac0b09a88b2a54494c35d172d109be28c51ed6048a6c496281","observation_id":"8fb5aa81-8558-46a6-b908-f544ad9d58ce","resolution":{"observed_at":"2026-08-06T15:29:09.380552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:14.780490Z","title":"Ser-fiq: Unsupervised esti- mation of face image quality based on stochastic embedding robustness","venue":null,"work_id":"b50255ba-1e08-40c0-ab2b-567545059683","year":2020},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.462066Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:704cef785bc85790df79cf36e98a8dc8ba0e9300d20de0e55bc780321e689b60","observation_id":"456641a8-432d-46fc-892f-c59c30d9c18a","resolution":{"observed_at":"2026-08-06T15:29:14.921307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:14.549968Z","title":"S3: A spectral and spatial sharpness measure","venue":null,"work_id":"0679003c-1f45-4993-95a8-7c23599820c8","year":2009},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.524568Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:fac72d33e10d26a9fac67e3b3892882af7c425c22753e1b01ee97dbe0615ff2f","observation_id":"9ab6c32e-1fab-4eec-bf9b-6b63219ece23","resolution":{"observed_at":"2026-08-06T15:29:14.683411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:14.334042Z","title":"Ex- ploring clip for assessing the look and feel of images","venue":null,"work_id":"0e330596-eaeb-4be8-8231-a7a9f740d655","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.603523Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:90b8de45ce60c8737bcacc4308e3074b707c3bccea89317466fc12c916a3de7a","observation_id":"dd5e0af3-cd6b-4b5b-ab92-33c05ef8001e","resolution":{"observed_at":"2026-08-06T15:29:14.457906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:14.174639Z","title":"Large multi-modality model assisted ai-generated image quality as- sessment","venue":null,"work_id":"427b386c-d50a-4874-96e5-76231b0967d9","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.668653Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:534671cb5ad89fd2dfc2787ac84a776fb8e251e46e2699ee39282c6f3e664e1c","observation_id":"fc8f06fb-da6b-4c28-ba98-da1c3827cf45","resolution":{"observed_at":"2026-08-06T15:29:14.250301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:14.025690Z","title":"Q-align: Teaching lmms for visual scoring via discrete text-defined levels","venue":null,"work_id":"84abed2a-a96c-41ef-b394-28831b92def9","year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.731394Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:f084feee60f365c638cecc68703bd23214c6d0e09f07e35bb88011511fa28f4c","observation_id":"dc5250f2-9ec6-436a-8dcb-b307e3f6ab54","resolution":{"observed_at":"2026-08-06T15:29:14.083722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:09.806049Z","title":"Fvq: A large-scale dataset and a lmm-based method for face video quality assessment","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.806049Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:0302ab9b6fd6fc0743fc5308e725a33cc9f4bb585830ae5b495414af37a5fdd5","observation_id":"ec9b3f12-ca7d-41ad-b3df-2c45517a12d2","resolution":{"observed_at":"2026-08-06T15:29:09.806049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:09.899849Z","title":"Self-training with noisy student improves imagenet clas- sification","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.899849Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:09a62eda1efca5c3aa55e3abfda55ccc108a4290cb72b99fbebcdcb7e3391eda","observation_id":"20b98541-ebba-4d3b-95b2-e4d47f3aa53d","resolution":{"observed_at":"2026-08-06T15:29:09.899849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.00546","last_updated":"2019-05-02T02:08:18Z","snapshot_observed_at":"2026-08-03T14:42:34.790631Z","submitted_at":"2019-05-02T02:08:18Z","title":"Billion-scale semi-supervised learning for image classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.00546","snapshot_observed_at":"2026-08-06T15:29:09.976461Z","title":"Billion-scale semi-supervised learning for image classification","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:09.976461Z"},"links":{"cited_paper":"/paper/1905.00546","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:ed035d332d235d74029e33ebd66ee9ebc0095a5ac2694f228f15af8116616994","observation_id":"5e9cb024-cd84-4420-8e4e-8b61b7ee6836","resolution":{"observed_at":"2026-08-06T15:29:09.976461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:10.067122Z","title":"Maniqa: Multi-dimension attention network for no-reference image quality assessment","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.067122Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b0d7fb2b986ed22e374fe7c25c17a1152b0f7b1d074be4bc88118e1fac224522","observation_id":"991cae4e-1f8b-4838-a9c5-413009f4d96c","resolution":{"observed_at":"2026-08-06T15:29:10.067122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:13.803189Z","title":"Attention based network for no- reference ugc video quality assessment","venue":null,"work_id":"c06c35cb-7b2a-4213-823b-311373950c73","year":null},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.147137Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:d24abd3dee9234661ee48f89ac760ca7bf1f79fe8826802a4ed1582385f170ee","observation_id":"30c80f60-7792-401a-9398-206ea9b3bc3d","resolution":{"observed_at":"2026-08-06T15:29:13.911550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:13.594794Z","title":"Perceptual image quality assessment: a survey","venue":null,"work_id":"90281bba-a540-4ff0-a1aa-949d9915e4f6","year":2020},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.217442Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:211d7cb2cff1f8357b9bf761c884b382a55e8ee6b300a5fc98f48c6c3f32221b","observation_id":"3ef4cc19-7318-464b-b930-7754277d1a0c","resolution":{"observed_at":"2026-08-06T15:29:13.707817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:13.391592Z","title":"A psychovisual quality metric in free-energy principle","venue":null,"work_id":"96ad3f64-9682-4f6b-a3cf-b81f3560be9d","year":2011},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.325537Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:06f35ad23b54006c6c5094f3405a1027b65cd75b5d4a1419c5fbe5f8bc9df076","observation_id":"d8094df4-be2b-450d-9811-7a5a6c925652","resolution":{"observed_at":"2026-08-06T15:29:13.476233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:13.094018Z","title":"Perceptual quality assessment of low-light image enhance- ment","venue":null,"work_id":"5a13fc12-4683-4296-886d-ede418025f5b","year":2021},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.387022Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:73b4abc968a57722793db2b70bcd8402c7f9395b528c9a02fa5cd533149aae51","observation_id":"9933eeac-22db-4b7d-9051-e4288077b811","resolution":{"observed_at":"2026-08-06T15:29:13.211664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:12.850865Z","title":"Blind image quality assessment via vision- language correspondence: A multitask learning perspective","venue":null,"work_id":"f94557a7-e738-4ffd-9244-03d113ef81ef","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.459568Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b2a6dcae0a2e43be1ac43ccd10ef93d869b24b6e0a22f13b8a174a970d9eb835","observation_id":"52abbc73-05c4-4558-b6a2-7ad858a42b5b","resolution":{"observed_at":"2026-08-06T15:29:12.961257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:12.544209Z","title":"A no-reference evalu- ation metric for low-light image enhancement","venue":null,"work_id":"b75218d1-8bd2-4898-a022-8bda7754f802","year":null},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.538007Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:b0cd0b8bcdc493136729a44f06298d00bd4df3ba5b363de9b26185ea33ce9b69","observation_id":"9a30edc4-5435-4460-964e-efb9ab60b23e","resolution":{"observed_at":"2026-08-06T15:29:12.660174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:12.239417Z","title":"A no-reference deep learning quality assessment method for super-resolution im- ages based on frequency maps","venue":null,"work_id":"9bce8c42-caab-4a69-8f99-e0a3d1cf37c8","year":2022},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.604625Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:10d7d051f907a000a06b9c5d41043bb18894676e8d50138e21d6e7036589f718","observation_id":"afbc08c0-5411-4200-8256-3755a0808f44","resolution":{"observed_at":"2026-08-06T15:29:12.404967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:11.995686Z","title":"Md-vqa: Multi-dimensional quality assessment for ugc live videos","venue":null,"work_id":"36b692f3-133d-4b5b-8f61-c1baed75d6b9","year":2023},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.674582Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:8ce45e8512aa3d815a9b36f68048858ef546bee8a9f081c6a764b69959148079","observation_id":"e3567aee-4f4b-4af0-bb95-74c89ee0009b","resolution":{"observed_at":"2026-08-06T15:29:12.114609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21408","last_updated":"2024-12-26T03:21:00Z","snapshot_observed_at":"2026-07-06T18:54:54.118506Z","submitted_at":"2024-07-31T07:54:26Z","title":"Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21408","snapshot_observed_at":"2026-08-06T15:29:10.750858Z","title":"Benchmarking multi-dimensional aigc video qual- ity assessment: A dataset and unified model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.750858Z"},"links":{"cited_paper":"/paper/2407.21408","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:310d9ac807beb2564a345297414d9c88be56ddf87d0951817bd3bdd0a6878de5","observation_id":"8d29674c-8c89-454a-88ac-98860673220b","resolution":{"observed_at":"2026-08-06T15:29:10.750858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16619","last_updated":"2025-07-23T08:05:06Z","snapshot_observed_at":"2026-08-06T07:32:16.470602Z","submitted_at":"2024-11-25T17:58:43Z","title":"Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.16619","snapshot_observed_at":"2026-08-06T15:29:10.862485Z","title":"Human-activity agv quality assess- ment: A benchmark dataset and an objective evaluation met- ric","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.862485Z"},"links":{"cited_paper":"/paper/2411.16619","citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:38e270aeaa2c2e8c55b0ae0689b6c2214cccda4c1af44aa3f7a7c2591a6f3155","observation_id":"f6520ac5-cb40-4d62-9de5-94d090731953","resolution":{"observed_at":"2026-08-06T15:29:10.862485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:11.798175Z","title":"Advancing zero-shot digital human quality assessment through text-prompted evaluation","venue":null,"work_id":"911db2ee-b060-4f32-ad51-360428bb8727","year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:10.925897Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:5a5b323863d220d5fbb7b87ddbb2c278e91a756109e9a885d8f2b5d2c845f530","observation_id":"3feb4ea5-b13f-44ff-9b97-99d98466a8d2","resolution":{"observed_at":"2026-08-06T15:29:11.892480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:29:11.031879Z","title":"Vquala 2025 challenge on visual quality comparison for large multimodal models: Methods and results","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:11.031879Z"},"links":{"citing_paper":"/paper/2507.15709"},"observation_digest":"sha256:654fa7e3cd697163fc06c03bc9f19ec08964df2e1b85049a98e303760abcd348","observation_id":"96581f73-bd66-413f-bda8-f9f61362e722","resolution":{"observed_at":"2026-08-06T15:29:11.031879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.15709","last_updated":"2025-08-10T13:31:03Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T08:52:34.323238Z","submitted_at":"2025-07-21T15:17:01Z","title":"Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":1,"verified_fuzzy":45},"total_outbound_references":67},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2507.15709."}