{"as_of":"2026-08-15T01:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4d2f7ad7056609f4f556f18f6006ac606c3ab2625fc48adfaa9d0780068662f5","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T04:21:06.339224Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T11:16:04.151281Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01555","snapshot_observed_at":"2026-08-03T11:16:04.151281Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.07048","last_updated":"2026-07-31T03:12:32Z","snapshot_observed_at":"2026-08-10T14:40:56.643466Z","submitted_at":"2026-01-11T19:51:54Z","title":"GPU-Accelerated ANNS: Quantized for Speed, Built for Change","version":5},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T11:16:04.151281Z"},"links":{"cited_paper":"/paper/2412.01555","citing_paper":"/paper/2601.07048"},"observation_digest":"sha256:4d6737ead6e3903c8740bb936f3d04d59cbfcf39a7fe46b52cbde1998ee97bce","observation_id":"bfa5d040-4d9e-46de-9e5f-7bc91a526434","resolution":{"observed_at":"2026-08-03T11:16:04.151281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.01555/citation-record","integrity":"/paper/2412.01555/integrity","json":"/paper/2412.01555/citation-record.json","paper":"/paper/2412.01555"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.131355Z","title":"AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.131355Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:1e68796b286851c660c83f71e9330fbad5dc408e7cc7e4557782e26c75b9123d","observation_id":"3eeb0445-dd26-4da8-90d7-bffb14051b10","resolution":{"observed_at":"2026-08-12T04:21:06.131355Z","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":"10.1023/a:1009804230409","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.562313Z","title":"E- Commerce Recommendation Applications,","venue":null,"work_id":"f68b6380-1466-466f-bc3f-96e6e023c100","year":2001},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.136753Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:81b10a15e4af1ee861bb8886462684cf7a97d0fbbba39fca8ac904b5176fbc28","observation_id":"7d16300a-5368-4df0-9b1d-71583ca4f556","resolution":{"observed_at":"2026-08-12T04:21:06.567274Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.10562","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:07.499052Z","title":"Content-based image retrieval for medical diagnosis using fuzzy clustering and deep learning,","venue":null,"work_id":"7a20a896-fc9b-4c6e-bfaa-509cbd97d80b","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.141930Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:2b0677df71a26bfc004a83af513f0459c0cc8b618427b55639a22efd13f83081","observation_id":"18ca531a-72ca-4311-9210-de9def255ceb","resolution":{"observed_at":"2026-08-12T04:21:07.508075Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:06.147380Z","title":"State-of-the-art in artificial neural network applications: A survey,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.147380Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:00a30bc685a4f9688a1524441c505a13ced0766ebd051e98809b7ed9c03c7833","observation_id":"2599a0a5-96a2-4e54-a7d6-2cf8a9d38fd9","resolution":{"observed_at":"2026-08-12T04:21:06.147380Z","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":"10.1016/s0167-7012(00)00201-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.529230Z","title":"Artificial neural networks: fundamentals, computing, design, and application,","venue":null,"work_id":"99006895-cbb1-45a6-ac80-b4239a2bcf16","year":2000},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.152394Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:e68582db96ae7a977401e07d263e22bba29c71d90edf3262d88d526d7393f210","observation_id":"f4c01bf6-8911-41d8-9279-77b9c2239a34","resolution":{"observed_at":"2026-08-12T04:21:06.534397Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2003.81247","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:07.411833Z","title":"A comprehensive review for industrial applicability of artificial neural networks,","venue":null,"work_id":"0ff6a1a6-dd4e-418e-9f88-0b3b60ef726a","year":2003},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.161199Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:e1bdd4fe23f2c290e781fb9e787d8db3aca0ade84dc5ecfed0bf4626a6989e97","observation_id":"b0ec20d9-79ba-497a-84b8-0091a57226f7","resolution":{"observed_at":"2026-08-12T04:21:07.420644Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08281","last_updated":"2025-10-23T09:36:08Z","snapshot_observed_at":"2026-07-31T05:45:37.385210Z","submitted_at":"2024-01-16T11:12:36Z","title":"The Faiss library","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08281","snapshot_observed_at":"2026-08-12T04:21:06.168007Z","title":"The Faiss library,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.168007Z"},"links":{"cited_paper":"/paper/2401.08281","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:974dac0877dd1a5f71392f0bb69d32a8033f72b7b3560f5d68821f038e41147f","observation_id":"6248712a-0012-41d1-b5a2-7f63615eb7a1","resolution":{"observed_at":"2026-08-12T04:21:06.168007Z","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":"2026-08-12T04:21:07.719397Z","title":"GitHub - spotify/annoy: Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk","venue":null,"work_id":"8aaa720b-ba91-4fdb-a080-7e85c36480e7","year":null},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.174423Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:e93df9d11e912811d808d08b673d9e60cbd7de97ae7fad0b0444f0851a2fee4b","observation_id":"f8969aa9-949d-4a29-adf1-1a60edc15526","resolution":{"observed_at":"2026-08-12T04:21:07.726025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1080/10286600600888565","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.512495Z","title":"Comparison of different ANN techniques in river flow prediction,","venue":null,"work_id":"ac613591-7c5a-42f5-b3da-c0ef68bb1fb4","year":2007},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.184633Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:05409f25bbd890c8092d01047cb328132b0c2f977c48245804cd00ea289b866a","observation_id":"c35b7996-e731-4453-952c-5946fc3fcc11","resolution":{"observed_at":"2026-08-12T04:21:06.518156Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.10531","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:07.298768Z","title":"Domain-specific language models pre-trained on construction management systems corpora,","venue":null,"work_id":"8393fb5f-aad5-4d1f-8174-75fbdbe66936","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.189934Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:761cbf4893627138c163fc646cf6cd0ad5284e8bc62f092b14daf2dd76e422b7","observation_id":"dc537241-8166-45e0-bc84-b4f75d3ce204","resolution":{"observed_at":"2026-08-12T04:21:07.307046Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:06.195852Z","title":"Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.195852Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:dd0f8b60f448efc22dfeda0986977e62f7e1c65e450fbaa81f33630bf8cf2f7a","observation_id":"46c97d5f-9e06-4b2b-8a8d-2ac5c9a621e0","resolution":{"observed_at":"2026-08-12T04:21:06.195852Z","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":"2026-08-12T04:21:06.207358Z","title":"Product quantization for nearest neighbor search,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.207358Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:9c8be84a45f537b5b81cfeb6dde1f3ad8c1681c4461fdce7ea86269c94a2494b","observation_id":"3d215bf0-4471-42ad-b8d1-56fdecd43cde","resolution":{"observed_at":"2026-08-12T04:21:06.207358Z","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":"2026-08-12T04:21:06.213410Z","title":"Billion-Scale Similarity Search with GPUs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.213410Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:7fa22a67c0f215c0c0a72e6c5b7d1819616526797dbec9a85644a2ecab4de22d","observation_id":"4a0b364f-9048-480c-9ae3-2b5459df42fa","resolution":{"observed_at":"2026-08-12T04:21:06.213410Z","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":"2026-08-12T04:21:07.683091Z","title":"Curator: Efficient Indexing for Multi-Tenant Vector Databases,","venue":null,"work_id":"cbc8ea6b-1acb-42d2-ac55-1507c325b30f","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.219064Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:67819ab52159bf7098d5b125cb5d9a562f1eb6a6ed078aeabc122fa98986dbf0","observation_id":"064e2b85-2c66-49c6-af46-470247ff26b3","resolution":{"observed_at":"2026-08-12T04:21:07.688133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.14958","last_updated":"2022-11-08T03:04:06Z","snapshot_observed_at":"2026-08-13T13:55:45.602633Z","submitted_at":"2022-10-26T18:15:47Z","title":"Constrained Approximate Similarity Search on Proximity Graph","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.14958","snapshot_observed_at":"2026-08-12T04:21:06.228854Z","title":"Constrained Approximate Similarity Search on Proximity Graph,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.228854Z"},"links":{"cited_paper":"/paper/2210.14958","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:346102d753d0212ccfba168131488e2c89d1072ead8eb8a6b45897c307dd26cf","observation_id":"06c932ac-33f8-47f1-8be4-30309f37cb88","resolution":{"observed_at":"2026-08-12T04:21:06.228854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07119","last_updated":"2024-01-13T17:08:09Z","snapshot_observed_at":"2026-08-14T21:41:41.753023Z","submitted_at":"2024-01-13T17:08:09Z","title":"Curator: Efficient Indexing for Multi-Tenant Vector Databases","version":1},"cited_work":{"arxiv_id":"2401.07119","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.07119","snapshot_observed_at":"2026-08-12T04:21:07.049150Z","title":"Curator: Efficient Indexing for Multi-Tenant Vector Databases","venue":"cs.DB","work_id":"a70266c0-0fa1-4d19-a281-8790e6f88ff7","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.223684Z"},"links":{"cited_paper":"/paper/2401.07119","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:58c747a6e69965ea3da891b0b4c9f3e274dc4aaf143559d8a1a77714555cb1a2","observation_id":"d1337513-e76e-497e-bc57-bd41e15b89f4","resolution":{"observed_at":"2026-08-12T04:21:07.054275Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.04359","last_updated":"2024-02-08T17:00:13Z","snapshot_observed_at":"2026-08-13T11:47:39.468674Z","submitted_at":"2023-05-07T19:28:23Z","title":"ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.04359","snapshot_observed_at":"2026-08-12T04:21:06.242199Z","title":"ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.242199Z"},"links":{"cited_paper":"/paper/2305.04359","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:a29953e156a76c5f6d4cb6bd9e062fe4ebf3a641cc729c1b58c2519d7c408ef3","observation_id":"d30811e6-11f6-4785-aeec-85bf6a260545","resolution":{"observed_at":"2026-08-12T04:21:06.242199Z","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":"2026-08-12T04:21:06.251909Z","title":"ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.251909Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:92a752dd8e085b49b7a2d9bc0b5d661cc09e24fd9ce20c84012048edcc982496","observation_id":"a2dc9727-fdc0-4a79-bbc6-f3686af7c1bc","resolution":{"observed_at":"2026-08-12T04:21:06.251909Z","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":"2026-08-12T04:21:06.247013Z","title":"A meta-learning configuration framework for graph-based similarity search indexes,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.247013Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:46ed8e41f6260d4c11882ae330fb2d97f242be5aafe38650cbeae0d286c3562f","observation_id":"872cdba6-eac8-4f60-ae06-5733863aa50b","resolution":{"observed_at":"2026-08-12T04:21:06.247013Z","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":"2026-08-12T04:21:06.262390Z","title":"An efficient faiss-based search method for mass spectral library searching,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.262390Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:1c65f5213c96708c0f68e8b61bc9f832ac8bb9d80ecfc4d1c84dad6315cb25c1","observation_id":"7b4e989e-2161-4865-ab1c-2679b7f69f58","resolution":{"observed_at":"2026-08-12T04:21:06.262390Z","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":"10.1007/978-3-030-27562-4_27","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Approximate Similarity Search with FAISS Framework Using FPGAs on the Cloud,","venue":"Lecture notes in computer science","work_id":"e006d694-164c-439d-834e-3333f5fc590c","year":2019},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.256983Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:0581c9fa4c704750e563bacafb33a3b77f207b4076ba82552a57e628d34fcafb","observation_id":"6083f1ea-eae7-41c8-9b23-afeea884a1d3","resolution":{"observed_at":"2026-08-12T04:21:06.468860Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:07.665208Z","title":"An Investigation of Practical Approximate Nearest Neighbor Algorithms,","venue":null,"work_id":"4036d49f-bb8b-4a41-9031-f8afd44d2a1d","year":2004},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.273441Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:85e54bc1b13137c53d564896332ffcd71a7d7bae67331cbbab5f07f47a2c552e","observation_id":"bd335fde-0aa2-4c6a-ab7f-75c4543ee69f","resolution":{"observed_at":"2026-08-12T04:21:07.670406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01473","last_updated":"2024-11-03T08:14:31Z","snapshot_observed_at":"2026-08-12T22:08:58.540908Z","submitted_at":"2024-11-03T08:14:31Z","title":"Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification","version":1},"cited_work":{"arxiv_id":"2411.01473","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.01473","snapshot_observed_at":"2026-08-12T04:21:06.774907Z","title":"Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification","venue":"cs.CV","work_id":"4d7b7d61-9bbd-455e-a423-d0f81d1910c2","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.268301Z"},"links":{"cited_paper":"/paper/2411.01473","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:75d3ae57b28d9900f17c88a06a5dd7230c7ab0a1fd4e6a3dd53c27f69f1c1020","observation_id":"ba742e9c-3423-40d6-97f3-e9a1ba9f2d7e","resolution":{"observed_at":"2026-08-12T04:21:06.780862Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/acs.jproteome.8b00359/suppl_file/","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.421075Z","title":"Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing,","venue":null,"work_id":"2de6f9cb-47ab-4941-a0fc-c07d37786f82","year":2018},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.283441Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:e60a9b316ae2221716e508d0010419276e82225167d2c8a7e6842fed7f226aca","observation_id":"4194691e-4a29-4dff-88ac-4631069b01ec","resolution":{"observed_at":"2026-08-12T04:21:06.426697Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00778-024-00864-x/metrics","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.439767Z","title":"Survey of vector database management systems,","venue":null,"work_id":"9723cc67-3fab-4f1a-b536-8f5f8faed5f9","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.278358Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:2749be959dc1e9ab2987e45fe511952cf3fbf34985333ad3fdf165d73ab0ee80","observation_id":"27b8c769-9b1d-4917-9796-ebf2190bb040","resolution":{"observed_at":"2026-08-12T04:21:06.445005Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:07.646341Z","title":"Fashion Product Images Dataset","venue":null,"work_id":"6a21a980-7dbf-456e-87d3-62d7ebcfc81a","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.293967Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:65d85d6fbe3ce6da61b560f15dc39a4876ec69a85e745f82497af0e3f85c5919","observation_id":"914e08ae-cb1f-4faf-915f-e0a68a64311c","resolution":{"observed_at":"2026-08-12T04:21:07.652432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:06.288478Z","title":"The role of local dimensionality measures in benchmarking nearest neighbor search,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.288478Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:8fb48e91e5bbdd1e54247ac66c36e5e60c0022c3a8c133a31718f6b96f5a4c5d","observation_id":"9e466b3a-3e4c-4dcc-b55c-06061dd9e145","resolution":{"observed_at":"2026-08-12T04:21:06.288478Z","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":"10.1007/978-3-540-76917-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.404006Z","title":"Advanced data mining techniques,","venue":null,"work_id":"a9bbf326-9543-43f3-82c2-a171a9d1591d","year":2008},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.303564Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:4ba1294a8a8c336f4e0c47286a1796240d42a4ffa3c73320a85acea95f7c7fd1","observation_id":"76e82348-1a32-4863-8110-2d525a8e2e67","resolution":{"observed_at":"2026-08-12T04:21:06.409329Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:07.626683Z","title":"Adafactor: Adaptive Learning Rates with Sublinear Memory Cost,","venue":null,"work_id":"142acbe2-3c5d-409a-a3c9-49466afd96f9","year":2018},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.298815Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:82ba9560cbf5ad0d4a2181414847f3533d87807f51143de20ab980ee48d0160b","observation_id":"81efbd4a-a85f-4cfe-9561-99d5419586bf","resolution":{"observed_at":"2026-08-12T04:21:07.633153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3606367/asset/91b75e27-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.387846Z","title":"A Review of the F-Measure: Its History, Properties, Criticism, and Alternatives,","venue":null,"work_id":"4d09b894-e5fa-4f5a-b73e-275694908506","year":2023},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.313739Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:7c207cc165c174b6e48dbb3bc3b46ce2619f7c4f8c5396cbc7829920b72faa6b","observation_id":"06502fa7-10be-4f84-b4c8-64930e5e3650","resolution":{"observed_at":"2026-08-12T04:21:06.393058Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.16061","last_updated":"2020-10-11T02:15:11Z","snapshot_observed_at":"2026-08-04T20:09:58.097029Z","submitted_at":"2020-10-11T02:15:11Z","title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.16061","snapshot_observed_at":"2026-08-12T04:21:06.308802Z","title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.308802Z"},"links":{"cited_paper":"/paper/2010.16061","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:125cf92a06ed98f20e81f3d2a0c4b941905daf4c7817ae6354624f01a25c20a9","observation_id":"fd6bcf22-d00e-4d6c-a726-0c132814dfeb","resolution":{"observed_at":"2026-08-12T04:21:06.308802Z","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":"2026-08-12T04:21:06.324062Z","title":"High-dimensional signature compression for large-scale image classification,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.324062Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:1c5c6415a310ae7fa4ace55bc0c1849c16e60dcdd6d8adb5345c82aa0e17413a","observation_id":"8b0226ca-8f5b-4ddc-8c6c-21cf8639617f","resolution":{"observed_at":"2026-08-12T04:21:06.324062Z","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":"10.1186/s12864-019-6413-7/tables/5","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:21:06.370092Z","title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,","venue":null,"work_id":"78bc07e6-a2de-40b3-9851-e16ba9d3ae84","year":2020},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.318505Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:6cdf7eaa936e05b5c3beb496faa3f117665695cc898ec08588143bcfb6dfbcc6","observation_id":"021eeffc-04db-4545-a82d-56a83196a953","resolution":{"observed_at":"2026-08-12T04:21:06.377606Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.07355","last_updated":"2018-10-17T02:22:34Z","snapshot_observed_at":"2026-08-14T18:13:28.911182Z","submitted_at":"2018-10-17T02:22:34Z","title":"Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.07355","snapshot_observed_at":"2026-08-12T04:21:06.334421Z","title":"Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.334421Z"},"links":{"cited_paper":"/paper/1810.07355","citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:a730e33f49b83c6a43020b10d224e896ce3f5a8f9f296d93ef0923c8bbe9dff4","observation_id":"493a622b-6353-47e1-b07d-2dced18b1ee4","resolution":{"observed_at":"2026-08-12T04:21:06.334421Z","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":"2026-08-12T04:21:07.607475Z","title":"Announcing ScaNN: Efficient Vector Similarity Search","venue":null,"work_id":"5ab53ce1-035a-4b1f-b500-8b4962197d6c","year":2024},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.328938Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:f24b4ffb6011ee3a0f131aa0dce5a3246fbc2d9f91657c3e24b9a81088ffb0bb","observation_id":"6f970e69-29de-414d-a166-10f99c512515","resolution":{"observed_at":"2026-08-12T04:21:07.612755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T04:21:06.339224Z","title":"Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.339224Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:f01c1da1536c06c781b296572b95aa2bf1a0a43d8c9b8bae6691d292270d3f68","observation_id":"e007fcf7-e564-410f-bcb2-306c8ccff071","resolution":{"observed_at":"2026-08-12T04:21:06.339224Z","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":"2026-08-12T04:21:07.699487Z","title":"Available: https://github.com/spotify/annoy","venue":null,"work_id":"1637c235-2bc5-4a03-b66f-3c67375fa834","year":null},"citing_paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-12T04:21:06.179580Z"},"links":{"citing_paper":"/paper/2412.01555"},"observation_digest":"sha256:90daaa7734be2eab74355dfed114168da6a3d2bd8a3bcca34edb228d7a8f68ac","observation_id":"b8387c3d-4e49-4ad3-b532-1360c1700466","resolution":{"observed_at":"2026-08-12T04:21:07.705899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.01555","last_updated":"2024-12-02T14:43:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T15:44:19.998637Z","submitted_at":"2024-12-02T14:43:06Z","title":"Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":4,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":16,"verified_exact":6,"verified_fuzzy":7},"total_outbound_references":37},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2412.01555."}