{"as_of":"2026-08-15T09:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4b0d6571f5a3de1173e1a30b656448e8f8c7383ed5f0bb75ff3c9ac323201793","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:14:33.266930Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2506.11122/citation-record","integrity":"/paper/2506.11122/integrity","json":"/paper/2506.11122/citation-record.json","paper":"/paper/2506.11122"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.259074Z","title":"A review of object detection techniques,","venue":null,"work_id":"14eb3a32-1eba-4031-a787-511985cc19d3","year":2020},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:31.826363Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:86d0de5826db0323dce341ee828560b11a3941cee7f1feabf843ead3e7c02eb0","observation_id":"32f42896-de07-40e0-8d8f-f5b6a87ff388","resolution":{"observed_at":"2026-08-07T05:14:34.264116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.05055","last_updated":"2023-01-18T14:23:50Z","snapshot_observed_at":"2026-08-14T16:33:12.134751Z","submitted_at":"2019-05-13T14:26:50Z","title":"Object Detection in 20 Years: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.05055","snapshot_observed_at":"2026-08-07T05:13:31.868972Z","title":"Object detection in 20 years: A survey,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:31.868972Z"},"links":{"cited_paper":"/paper/1905.05055","citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:f1a350a53636b89443a10a23c4c12e972f6ae8003d1b3bc756988448827913f9","observation_id":"2bfc88e6-9672-449b-8d1d-c8176fe367bb","resolution":{"observed_at":"2026-08-07T05:13:31.868972Z","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":"2026-08-07T05:13:31.934273Z","title":"Imagenet classification with deep convolutional neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:31.934273Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:5baf0e0ec76ae86d338d15d7a4ccb933c29c70c85aebf7f836a5cc3ced33dbdf","observation_id":"af3d2e46-909d-4ca5-8c98-14bd03ebe2d9","resolution":{"observed_at":"2026-08-07T05:13:31.934273Z","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":"10.5815/ijigsp.2022.02.05","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Recent object detection techniques: A survey,","venue":"International Journal of Image Graphics and Signal Processing","work_id":"ddd91f14-0bb4-4c9e-bc6c-22c508368277","year":2022},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:31.983493Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:b23069ad79ea2ad8c96c661e9ee0067c6441b99a757c78d401ba128c4e91c588","observation_id":"369041db-d376-4337-9bcb-32d6001bcb7a","resolution":{"observed_at":"2026-08-07T05:14:33.628897Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11263-019-01230-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.610950Z","title":"Deep learning for generic object detection: A survey,","venue":null,"work_id":"e894e5dd-fe5a-40fa-9098-324857f87279","year":2020},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.058959Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:2a64f1ad4c088df3ab52489332fb1529882e968f531e4fe19644717d7ab7c7f4","observation_id":"bf299e61-99f1-42cd-bcf6-e3017afab7fc","resolution":{"observed_at":"2026-08-07T05:14:33.615067Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/8957477","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.189035Z","title":"Object detection techniques: Overview and performance comparison,","venue":null,"work_id":"086488fa-808b-4669-b9da-d61402794f8d","year":2019},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.163388Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:7c48c7a5cae9fbcbfad7a13f8c53c2de5981d714dd42903107b8d2dbac75a342","observation_id":"e4f8e530-3f33-4957-906f-9b839dc33404","resolution":{"observed_at":"2026-08-07T05:14:34.197082Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:13:32.247898Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.247898Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:0f765c7e6040202618f5d0b7efd136af5a770bbfc350d7596c3ff7533ab568a7","observation_id":"190b060e-06aa-49f9-8055-9a6542039da2","resolution":{"observed_at":"2026-08-07T05:13:32.247898Z","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":"2010.20877","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.012959Z","title":"Image resolution enhancement by using discrete and stationary wavelet decomposition,","venue":null,"work_id":"856e7fd6-604f-47fd-be86-9ae1e75af0af","year":2011},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.337999Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:3e932c758b881c88f50089e9406d196a50e16763f9cd300f54959e4e3ba1dc1a","observation_id":"809f80e9-5446-4a06-902a-223024158b64","resolution":{"observed_at":"2026-08-07T05:14:34.023740Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5565/rev/elcvia.523","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Detection and classification of multiple objects using an RGB - D sensor and linear spatial pyramid matching,","venue":"ELCVIA Electronic Letters on Computer Vision and Image Analysis","work_id":"9fa6adef-5961-4007-8c73-5f21296e67cd","year":2013},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.485513Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:e376799e949967588d222ea30d80f7fe7970379552e7ac437b9721198a45b6cf","observation_id":"639acf88-0b6d-44a6-bb2c-d39edd85a2a1","resolution":{"observed_at":"2026-08-07T05:14:33.601135Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1097/cm9.0000000000000095","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Artificial intelligence system of faster region -based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer,","venue":"Chinese Medical Journal","work_id":"62c74f6e-f27e-48a1-ab46-d8bfbaa18dac","year":2019},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.587252Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:eb91ad047605431cc3c5d9092fc46273b400abcc5d243064afd3def8b826c555","observation_id":"5dc75739-6de0-4f05-94aa-d33558688d88","resolution":{"observed_at":"2026-08-07T05:14:33.586957Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2014.69492","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.940755Z","title":"Super resolution image reconstruction using wavelet lifting schemes and Gabor filters,","venue":null,"work_id":"ba8cd1ab-d658-476f-a5a3-2a0a54e4bdaf","year":2014},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.673121Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:177139cfa0eff7a806ebe7a985ea9986c1c9f76138bd240d38b04335ab3f4fda","observation_id":"2af7791d-aca2-4d2f-af75-aa8fe14042f3","resolution":{"observed_at":"2026-08-07T05:14:33.948480Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:13:32.777333Z","title":"Object detection with discriminatively trained part -based models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.777333Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:921b57bae8137a4a89ba285ff751fc632eb9153f3a9ea753e806b87087265094","observation_id":"b14f27b9-7e16-4a7c-a53b-8f2515a5708b","resolution":{"observed_at":"2026-08-07T05:13:32.777333Z","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":"2026-08-07T05:13:32.844328Z","title":"Vehicle overtaking hazard detection over onboard cameras using deep convolutional networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.844328Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:8fdf115f3201b823ff600ff498df2b7c4d5deb8001375aa7d46f9bbec30ce324","observation_id":"1ce88f77-cffa-45b2-8b36-86e0efb2c598","resolution":{"observed_at":"2026-08-07T05:13:32.844328Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T05:13:32.908199Z","title":"Fast R -CNN,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.908199Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:9e1eca817dec47c0ff9b87a55426f091878d79e9273c801fefc0b05033a8bf30","observation_id":"a36a12d3-727c-41d2-b404-3914b866896d","resolution":{"observed_at":"2026-08-07T05:13:32.908199Z","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":"2026-08-07T05:13:32.979190Z","title":"Mask R-CNN,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:32.979190Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:72b2622634d61ddf659fa70e167eaad193ecd38d010147300410e1000f737825","observation_id":"d3ffe5ec-a604-4f56-a845-853861dcab08","resolution":{"observed_at":"2026-08-07T05:13:32.979190Z","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":"2026-08-07T05:13:33.081329Z","title":"A comprehensive survey of video datasets for background subtraction,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.081329Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:268b70ca54235004edc89fce12553156511f933822da1a37d440c632c21c0c07","observation_id":"065fd6db-2469-4bfd-b25f-4aaf028aec8d","resolution":{"observed_at":"2026-08-07T05:13:33.081329Z","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":"10.1088/1755","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.531805Z","title":"Evaluation of the accuracy of oil palm tree detection using deep learning and support vector machine classifiers,","venue":null,"work_id":"ed410e7e-309d-4a20-bf3e-fa4f364d0741","year":2022},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.177976Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:7dc8be732eb8d9ced8c3139f5f5229b995112e09dcc4a2770289c1ea9ac6bcfb","observation_id":"101dcf80-8b7b-4d1b-bfd2-0f418d4b436f","resolution":{"observed_at":"2026-08-07T05:14:33.535808Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:13:33.291165Z","title":"Accurate image super -resolution using very deep convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.291165Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:60ff51424147ef33f9ba214f52c08e345f32f5a2ad250ca0a2f014169392c116","observation_id":"8a2696b1-3562-42d9-af31-87350563d86b","resolution":{"observed_at":"2026-08-07T05:13:33.291165Z","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":"10.3390/ecsa","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.508948Z","title":"Image resolution enhancement using convolutional autoencoders,","venue":null,"work_id":"9361f499-649f-440d-914a-90a94a9d673d","year":2020},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.362406Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:35ee54b37cc2494c595230a64880c50194b99e4743920977c62e8c3b94251268","observation_id":"62541de6-da75-4b05-aa23-e2bae3efd393","resolution":{"observed_at":"2026-08-07T05:14:33.513977Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/sdtp.12243","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"63‐3: real‐time spatial‐based projector resolution enhancement,","venue":"SID Symposium Digest of Technical Papers","work_id":"93d3d5f9-36a7-449e-b1ba-02991dcdf4f4","year":2018},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.472634Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:cb5434a02037122e5a20ad6437ee0dc1b995f5999a05b0d621d22fe6d8c09060","observation_id":"09e4991f-28de-417d-8e95-4a00bbc0790e","resolution":{"observed_at":"2026-08-07T05:14:33.499290Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:13:33.575070Z","title":"Deep learning vs. traditional computer vision,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.575070Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:857e4e99895e1a72dcb4d7eaa1585408d40cb11313dc4b56ede6ba8e6c538520","observation_id":"87299f7a-dfd3-46c5-973e-4a8f309c1621","resolution":{"observed_at":"2026-08-07T05:13:33.575070Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5120/ijca2018916708","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A review: object detection using deep learning,","venue":"International Journal of Computer Applications","work_id":"80983d46-8764-41f7-9dfe-7f3d8f55e1c4","year":2018},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.663587Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:3e51daf82f94fb67aa2dbab2083c51cd3826040edb658cb51fafa7b0d97863e9","observation_id":"394af2c0-859d-428b-a3c8-7c98f566904d","resolution":{"observed_at":"2026-08-07T05:14:33.473860Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:13:33.746342Z","title":"Frustum PointNets for 3D object detection from RGB-D data,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.746342Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:10869b28c0522732f521078a13e4eed04ff136b4cec1af339808e536e0f165ad","observation_id":"42b3535c-aa7b-41ba-9acd-1cac8c86afbb","resolution":{"observed_at":"2026-08-07T05:13:33.746342Z","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":"10.32377/cvrjst0712","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Comparative analysis of various enhancement methods for satellite images,","venue":"CVR Journal of Science & Technology","work_id":"c071988f-5325-4c35-8600-091fe0ea20df","year":2014},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:13:33.872043Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:e777add95a4701d956b38481caa0f0e2d6ec96a978b5bb071778e2faa34dc015","observation_id":"f52007f6-c557-4792-9125-0ce31753b22e","resolution":{"observed_at":"2026-08-07T05:14:33.459453Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.28989/compiler.v10i1.946","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Fish detection using morphological approach based -on K -means segmentation,","venue":"Compiler","work_id":"16089658-42c7-407f-91f7-bcd4a83849c3","year":2021},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.190506Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:316bec5f932c19d4525fe38cdc450bbfd9d957f84a6eae58093c499bbd3bf369","observation_id":"6393935f-5b14-46c3-9b31-0ed6a170e03a","resolution":{"observed_at":"2026-08-07T05:14:33.444966Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5120/903","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.424787Z","title":"PSO -based Tsallis thresholding selection procedure for image segmentation,","venue":null,"work_id":"a30b9360-05c4-493a-8323-41f1f937b882","year":2010},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.209497Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:e94c900b033aa36c05ee828d76c9b50474c163e205a85ce51e2fbd0e8a08ef1c","observation_id":"6f785406-d527-4d45-9594-9273a6ebe4a0","resolution":{"observed_at":"2026-08-07T05:14:33.429943Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:14:33.222009Z","title":"Deep learning techniques for image recognition and object detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.222009Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:605c9ad33d151477b467eafc9b0906b40b672f98c464bd8841bddf330c0c8b8c","observation_id":"63a25cfc-51b5-48f5-83c7-1592e4fff283","resolution":{"observed_at":"2026-08-07T05:14:33.222009Z","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":"10.3390/electronics13122298","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Colorectal polyp detection model by using super - resolution reconstruction and YOLO,","venue":"Electronics","work_id":"9dc79df5-044c-444d-a251-fb3ad433afae","year":2024},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.229419Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:e900a2a5fad13145292d6941ee18975d77bf371454781200da746809215ecda8","observation_id":"466e5138-e771-47a1-9588-9e79e6a7f93b","resolution":{"observed_at":"2026-08-07T05:14:33.414512Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/s17112557","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Super-resolution of plant disease images for the acceleration of image -based phenotyping and vigor diagnosis in agriculture,","venue":"Sensors","work_id":"7e594a65-a22a-4eec-9328-13f1de80f7aa","year":2017},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.235344Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:c6bd60c1f66e0f78c3dec6eae53341fa50be2b8f7fd06ad33388d1b8b64eb781","observation_id":"230abb21-56a1-4287-ad22-e8c37f583837","resolution":{"observed_at":"2026-08-07T05:14:33.399701Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/12.2207440","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Combined self -learning based single -image super - resolution and dual-tree complex wavelet transform denoising for medical images,","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","work_id":"00f8d8b6-5a90-4dc4-aa85-5ac9c71839a8","year":2016},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.241504Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:f307bde718980c63e1ee11f7540b09d97dcb863349efdb7b8da80e3bd783c03f","observation_id":"45e5ba3d-407a-4e43-beb7-4330479dfdcf","resolution":{"observed_at":"2026-08-07T05:14:33.352410Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/12.2680067","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Unsupervised vehicle extraction of bounding boxes in UA V images,","venue":null,"work_id":"1a6a1065-9948-4689-b045-8c1f5b3b3989","year":2023},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.246191Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:623d1a0870b096cb784477ce1cc4309d6e46af1251b3c6908afd9955d978916d","observation_id":"2bada922-ce3b-43be-bc3e-a83f8403daa6","resolution":{"observed_at":"2026-08-07T05:14:33.324200Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02132","last_updated":"2020-04-21T04:32:36Z","snapshot_observed_at":"2026-08-09T03:04:34.867522Z","submitted_at":"2020-04-21T04:32:36Z","title":"3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds","version":1},"cited_work":{"arxiv_id":"2005.02132","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.02132","snapshot_observed_at":"2026-08-07T05:14:33.652438Z","title":"3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds","venue":"cs.CV","work_id":"5e0d1cea-9e23-46ad-bb85-487c86b40d21","year":2020},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.252956Z"},"links":{"cited_paper":"/paper/2005.02132","citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:7b7bef7e94ad3d2f70b0d09dfb2464304d738db486ece99edc12bb34383aefc1","observation_id":"aaa0199c-4d58-4c40-b109-60a74499530b","resolution":{"observed_at":"2026-08-07T05:14:33.656863Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/12.2512576","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Lesion focused super -resolution,","venue":null,"work_id":"a7425fde-e6aa-410d-a649-ebb0fe2a494c","year":2019},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.258155Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:955fa2d9c75d22d6256dc81c25b0a40f7a26827484a054ad9964cb83e952d4e1","observation_id":"6aae5c86-95f3-4966-9644-49015cc6c6da","resolution":{"observed_at":"2026-08-07T05:14:33.304385Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:14:34.242586Z","title":"Microsoft Coco : Common objects in context,","venue":null,"work_id":"dbf00378-ca30-4e09-802e-9f8717f6b47d","year":2014},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.262799Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:8efc1b2fa8f919e43c427b5d70410dddc46e4592297be524437d8ab09d0470bf","observation_id":"4da03181-8bb5-43d0-b14a-9470909ec26a","resolution":{"observed_at":"2026-08-07T05:14:34.248427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T05:14:34.226908Z","title":null,"venue":null,"work_id":"53f1a6d6-7029-4c88-9fcb-d7fe7fdd9367","year":null},"citing_paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.266930Z"},"links":{"citing_paper":"/paper/2506.11122"},"observation_digest":"sha256:e47babbc805dc8668e49f078b4cf20e46dbaad391c4c16cff71c2cd996061bd4","observation_id":"913947bc-dc8b-4b3c-acc9-4cb187110765","resolution":{"observed_at":"2026-08-07T05:14:34.231581Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.11122","last_updated":"2025-06-10T05:49:54Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T06:26:19.533771Z","submitted_at":"2025-06-10T05:49:54Z","title":"Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":5,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":11,"verified_exact":15,"verified_fuzzy":2},"total_outbound_references":35},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.11122."}