{"as_of":"2026-08-07T06:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f5027a722b1595856ef9e9764df4446e55b16d7dc12ab3462a7988fc998551cc","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:53:30.508953Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.04412/citation-record","integrity":"/paper/2507.04412/integrity","json":"/paper/2507.04412/citation-record.json","paper":"/paper/2507.04412"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:53:44.320802Z","title":"Fruitq: a new dataset of multiple fruit images for freshness evaluation","venue":null,"work_id":"da9346c9-ffe1-4201-b8a5-3bb3a00958a6","year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.223424Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:894417f9762a8e0b049d614a20a32218fff734313553d14840a2ea74b6c2a849","observation_id":"cd67c55a-805a-4941-b87b-1e6310ee3be4","resolution":{"observed_at":"2026-08-06T19:53:44.476601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:44.038405Z","title":"Live to eat and eat to live longer","venue":null,"work_id":"31ea952a-41db-4a8b-bfd5-d52e3c21eb27","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.279179Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:d2e32a573b096b37fb3b36be46abc501b4204fb357210b2d4dcc5277b6b3de60","observation_id":"21d3c249-8a86-417e-ad86-1fbccc82679b","resolution":{"observed_at":"2026-08-06T19:53:44.159848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.10545","last_updated":"2020-08-24T16:33:37Z","snapshot_observed_at":"2026-07-06T09:49:44.919864Z","submitted_at":"2020-08-24T16:33:37Z","title":"Products-10K: A Large-scale Product Recognition Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.10545","snapshot_observed_at":"2026-08-06T19:53:23.364096Z","title":"Products-10k: A large-scale product recognition dataset","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.364096Z"},"links":{"cited_paper":"/paper/2008.10545","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:5e4c2b47feb1e8b59cf665c4e32999b86190283b220ba5b46d4ee4a4cc10f407","observation_id":"f0ad4be2-2bd7-4f7b-a3f4-addacd6dfd6d","resolution":{"observed_at":"2026-08-06T19:53:23.364096Z","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":"2026-08-06T19:53:43.794242Z","title":"Recipenlg: A cooking recipes dataset for semi-structured text generation","venue":null,"work_id":"d532bc19-0c05-4e9a-ad37-770e3c9ec08e","year":2020},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.447850Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:490655578a33e35f69e68f4c5cd894172f00c628586dc36e537eb445234efb18","observation_id":"b606cedb-a9de-4038-87dc-9d6ad317d717","resolution":{"observed_at":"2026-08-06T19:53:43.911878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:43.443183Z","title":"Food-101 – mining discriminative components with random forests","venue":null,"work_id":"8c7ff1a8-980e-4ecf-90b2-71f4e2ae46e3","year":2014},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.565768Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:b41deff21f7f287f9ea87e3ec27139d302c401000702254f293d474efcb8e16d","observation_id":"a02941e6-233f-4f96-9d14-5db765c7ce2e","resolution":{"observed_at":"2026-08-06T19:53:43.600893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:43.142877Z","title":"Food-101–mining discriminative components with random forests","venue":null,"work_id":"16cdae85-dfeb-4343-83a0-1a0b093fbb0e","year":2014},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.692678Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:b3a41d5b74ca4827869cb346347797f9da6cc917e499652c86c2ff52d181c2db","observation_id":"1192db75-84ba-49b4-a419-65b5d74653b5","resolution":{"observed_at":"2026-08-06T19:53:43.286992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:42.878226Z","title":"Bs-nets: An end- to-end framework for band selection of hyperspectral image","venue":null,"work_id":"286ede80-cb24-4fbc-9fcc-35495a6d52f0","year":1969},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.772653Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:a806eba0aae6b21608dba2ca80e9154ece8b038831c5c65aaae693deb3c1556b","observation_id":"915d8674-0fc3-4836-a6a6-1e82ee673fc2","resolution":{"observed_at":"2026-08-06T19:53:42.989371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:42.541616Z","title":"Cascade r-cnn: High quality object detection and instance segmentation","venue":null,"work_id":"1e3613e2-9da0-4016-bafc-96d8e056e659","year":2019},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:23.977117Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:c33d4b24140a3bb62b2da19236141970e94cf95e53f2c34012b83fca72530173","observation_id":"6e1863e3-b0c1-4618-8633-c68fd1a15e96","resolution":{"observed_at":"2026-08-06T19:53:42.681420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:42.222359Z","title":"Deep-based ingredi- ent recognition for cooking recipe retrieval","venue":null,"work_id":"eff1ea33-0c98-41f3-842c-8d42194d368a","year":2016},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:24.139150Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:cf1e2301671bb8e90a30f7c76f83e51e32ee143247a7325f3b533e19b0d924f3","observation_id":"cf6bb4c1-e44a-4f63-857c-2df9893b8e1b","resolution":{"observed_at":"2026-08-06T19:53:42.359089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:41.972312Z","title":"Hybrid task cascade for instance seg- mentation","venue":null,"work_id":"cf153774-4457-4b9e-a499-35cf0d2a0df4","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:24.360359Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:da6be9cd350120d42adbbf6a8641516c3b566118e94d0ef6a11e6131f80f1c11","observation_id":"ac2b869c-e48e-4288-9850-efa720ac09c7","resolution":{"observed_at":"2026-08-06T19:53:42.100828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:41.662223Z","title":"Beverage products packaging dataset for auto- matic shelf recognition and its application","venue":null,"work_id":"fb0f46ba-656b-4bfe-98e0-0e1b31ccb98a","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:24.506115Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:5ba7ea2e96b4a8d2873d9f8a217297b04cdd4e2a2b9bacf54d9d1c11d67caca7","observation_id":"e15b7d9e-d6f8-43db-88f4-45affa915a1e","resolution":{"observed_at":"2026-08-06T19:53:41.802715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:41.351230Z","title":"Fire: Food image to recipe generation","venue":null,"work_id":"25f85050-ad35-4456-b29f-df86c9e81c40","year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:24.632197Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:6f4add1f87ffe1bf0f0a2c69a2456f3a83461f35862b2fb63c34328a416bc663","observation_id":"f91378ed-08d4-46dd-b6b7-4017f8f3c1b0","resolution":{"observed_at":"2026-08-06T19:53:41.492709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:41.106914Z","title":"A low-shot object counting network with iterative prototype adaptation","venue":null,"work_id":"6cb0e836-8834-4297-91f2-e337d1437620","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:24.723190Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f99cc62c77384192dbebfa0d92589fd392e5657658ae8a47dc8f8d8453d0af96","observation_id":"ac3c4f76-364e-4167-a5b0-31cd822b9dcb","resolution":{"observed_at":"2026-08-06T19:53:41.222233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:40.785652Z","title":"Retrieval and classi- fication of food images","venue":null,"work_id":"10633308-25f8-4649-bf48-a7e9904f8280","year":2016},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:24.894690Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:5a0f42342a50348aa224fdf81ff8380f460d00a7c1acbb63ee1b5b93f3ee8cfa","observation_id":"c83bc18c-0b7f-4459-994d-a65ce41fe839","resolution":{"observed_at":"2026-08-06T19:53:40.946980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.09285","last_updated":"2024-07-12T14:15:48Z","snapshot_observed_at":"2026-07-06T18:45:22.812592Z","submitted_at":"2024-07-12T14:15:48Z","title":"MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.09285","snapshot_observed_at":"2026-08-06T19:53:25.069335Z","title":"Metafood cvpr 2024 challenge on physically informed 3d food reconstruction: Methods and results","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.069335Z"},"links":{"cited_paper":"/paper/2407.09285","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:0769d08dce020378f87a82c0b155a6885adda97fa26c73d4a6edfe19b8aae3ad","observation_id":"77230496-02f0-4345-bf9a-7cba348edf55","resolution":{"observed_at":"2026-08-06T19:53:25.069335Z","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":"2026-08-06T19:53:40.558606Z","title":"Mask r-cnn","venue":null,"work_id":"b4d737e0-0144-4dca-8b17-b1b3e22b98c8","year":2017},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.168093Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:06b1d9de692db581fd94de3829ec7d972571193f0e831fe68c86f920f49a1c8f","observation_id":"53788b3a-0429-4150-8279-474062e8303a","resolution":{"observed_at":"2026-08-06T19:53:40.657074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10203","last_updated":"2022-09-05T18:03:11Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T14:26:38Z","title":"Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10203","snapshot_observed_at":"2026-08-06T19:53:25.268055Z","title":"Learning to count anything: Reference-less class-agnostic counting with weak supervision","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.268055Z"},"links":{"cited_paper":"/paper/2205.10203","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:6f40407451648105a42626d2b100e57c10fea515f485e90d0596dabb2bde5e80","observation_id":"f4a36621-0aee-4fa1-bd05-7e9709d0cd52","resolution":{"observed_at":"2026-08-06T19:53:25.268055Z","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":"2026-08-06T19:53:40.273581Z","title":"Vegfru: A domain-specific dataset for fine-grained visual categoriza- tion","venue":null,"work_id":"9451f727-6df3-4cb3-a8bc-d3c34b77c8b3","year":2017},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.411993Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:6bfaa7ad0e4e84db144ef3041180905f71ce3da97a1f9fbabc408684064b5a0a","observation_id":"af555b54-8fda-49e5-8dd6-7fa72c130401","resolution":{"observed_at":"2026-08-06T19:53:40.358388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:40.091108Z","title":"One-shot neu- ral band selection for spectral recovery","venue":null,"work_id":"de7f72c5-2de5-4559-86f6-f602d9d5dbf8","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.517891Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:2a6bc0231bd34bb1227777a0b6b104cea4f9f37a623bc67d61af998ab0cd206d","observation_id":"a210059f-a7bd-40fc-9ded-6fdf8970425f","resolution":{"observed_at":"2026-08-06T19:53:40.218620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:39.844544Z","title":"Mask scoring r-cnn","venue":null,"work_id":"e6bb7fc5-bed4-4797-98ca-4ca06369560c","year":2019},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.607297Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:e27c545a70b12a018b4a77c4d2d872cffbfc42795a0ac3d99cbbb807bc831328","observation_id":"54d6d49d-4b18-4f0b-b0a8-40277d67cd46","resolution":{"observed_at":"2026-08-06T19:53:39.957205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10084","last_updated":"2023-05-17T09:39:01Z","snapshot_observed_at":"2026-08-04T15:05:54.405123Z","submitted_at":"2023-05-17T09:39:01Z","title":"CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture","version":1},"cited_work":{"arxiv_id":"2305.10084","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.10084","snapshot_observed_at":"2026-08-06T19:53:31.589976Z","title":"CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture","venue":"cs.CV","work_id":"bf9067dc-b3c8-4010-b4c1-0b90ffbd8845","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.691919Z"},"links":{"cited_paper":"/paper/2305.10084","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:c9a3d4604f330aa65b61f77556754a4c0310e0c9f8d34582be347870ab4ab443","observation_id":"8af477e4-f0b9-4ebf-b8b0-a88bbf533ab0","resolution":{"observed_at":"2026-08-06T19:53:31.662800Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:39.616503Z","title":"Visible imaging to convolutionally discern and authenticate varieties of rice and their derived flours","venue":null,"work_id":"7cec6968-e667-42dd-8d0e-e7133eafbeaf","year":2020},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.762549Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:66c85abc7b29e1f53f4a04ea275a9cd2325bde29e28f0a5c6b1ff2b506e21e89","observation_id":"e3d4a9e6-7666-432a-acbf-c3d9f41cf6c9","resolution":{"observed_at":"2026-08-06T19:53:39.736409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12730","last_updated":"2024-12-16T06:16:27Z","snapshot_observed_at":"2026-08-05T22:34:04.983688Z","submitted_at":"2024-07-17T16:49:34Z","title":"RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12730","snapshot_observed_at":"2026-08-06T19:53:25.861113Z","title":"Rode: Linear rectified mixture of diverse experts for food large multi-modal models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.861113Z"},"links":{"cited_paper":"/paper/2407.12730","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f4570a23eb399482377793f7f3b2470c38762c199f1f4959700a7fee73fd424a","observation_id":"74b033b2-88f1-465f-b6e8-e1c5920645c9","resolution":{"observed_at":"2026-08-06T19:53:25.861113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.06167","last_updated":"2019-07-14T05:01:31Z","snapshot_observed_at":"2026-07-06T08:07:34.783333Z","submitted_at":"2019-07-14T05:01:31Z","title":"FoodX-251: A Dataset for Fine-grained Food Classification","version":1},"cited_work":{"arxiv_id":"1907.06167","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.06167","snapshot_observed_at":"2026-08-06T19:53:31.289347Z","title":"FoodX-251: A Dataset for Fine-grained Food Classification","venue":"cs.CV","work_id":"bb0a7752-a039-4745-9837-50734842e939","year":2019},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:25.956885Z"},"links":{"cited_paper":"/paper/1907.06167","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:3919c931d4ed4c24ff7063dde1d624f0bc6061e4bc50e46198a4522bffd0f690","observation_id":"2df4da36-6feb-4261-bb96-8608bf476906","resolution":{"observed_at":"2026-08-06T19:53:31.379668Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:39.429797Z","title":"Automatic expansion of a food image dataset leveraging existing categories with domain adaptation","venue":null,"work_id":"716dc01e-4c87-4232-aa90-4d7552d5fd76","year":2014},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.084982Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:d88d6e0825ed4a7bd73e86cb4c346008a56029e3bd52abf0533ff2e6fce19a5c","observation_id":"e82665b2-9998-4311-bc46-725cee684806","resolution":{"observed_at":"2026-08-06T19:53:39.494175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:39.177011Z","title":"A hierarchical grocery store image dataset with visual and se- mantic labels","venue":null,"work_id":"a28ec178-be85-426c-8299-0b9c241b5ab4","year":2019},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.204033Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:e9b17fba0bd10883a849f41d44eb53fe50be6b49f3bafd9850a2209243658927","observation_id":"98e40872-d594-4ece-b25b-dd867aa6d70a","resolution":{"observed_at":"2026-08-06T19:53:39.334140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.13721","last_updated":"2023-06-02T07:51:22Z","snapshot_observed_at":"2026-07-06T13:46:29.896484Z","submitted_at":"2022-08-29T17:02:45Z","title":"CounTR: Transformer-based Generalised Visual Counting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.13721","snapshot_observed_at":"2026-08-06T19:53:26.321670Z","title":"Countr: Transformer-based generalised visual count- ing","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.321670Z"},"links":{"cited_paper":"/paper/2208.13721","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:cfef022ee81ef15c05b84eda8fc7d848b22acfaf5da2d713a5a43f815faf188b","observation_id":"db3cc303-bb6a-401d-92a4-10614c58fe16","resolution":{"observed_at":"2026-08-06T19:53:26.321670Z","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":"2026-08-06T19:53:38.937967Z","title":"Ingredient prediction via context learn- ing network with class-adaptive asymmetric loss","venue":null,"work_id":"7493f31b-ad69-41df-b853-5ea99748d3e1","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.458308Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:67c6cee37fb6f9cbb0548abb1ae938554bdbe21d7675fd5cbf74a0adfa9bb4bb","observation_id":"655a8b2f-c5bb-4212-bc1b-757acbce9bb9","resolution":{"observed_at":"2026-08-06T19:53:39.046123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:38.768603Z","title":"Recipe1m+: A dataset for learning cross-modal embeddings for cooking recipes and food images","venue":null,"work_id":"8917dfad-6778-48fb-8fad-4b6952b58c1d","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.542477Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:c73b1dff957d1ff80a7b67c7ff73e49915066d0a618139926384b0fb08bbda8f","observation_id":"fca39ed9-5124-4894-bbcf-16ec3630a1d5","resolution":{"observed_at":"2026-08-06T19:53:38.841539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:38.628296Z","title":"Fruitnet: Indian fruits im- age dataset with quality for machine learning applications","venue":null,"work_id":"c1b2844e-432b-4314-8d83-2c99bd03074d","year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.667179Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:eed14b6fe2cf19dd3e7af47a44d14d1b5bae65f4e979f5baa9ffc1b7a20e6112","observation_id":"1932c532-7b48-48d7-9228-b1e13c1dbfb8","resolution":{"observed_at":"2026-08-06T19:53:38.681540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:38.312975Z","title":"Isia food- 500: A dataset for large-scale food recognition via stacked global-local attention network","venue":null,"work_id":"166cddea-ab8a-478e-98d2-222a0ddd8708","year":2020},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.801394Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:fe3ec3c21bc153a255d8ddc488a6fa007c16555db6ada42ca4ebb89bde1a210c","observation_id":"7897c78d-d13b-409e-8bb8-ea5e06cd0ef6","resolution":{"observed_at":"2026-08-06T19:53:38.464346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:38.059340Z","title":"Large scale visual food recognition","venue":null,"work_id":"1a56074f-82a6-4339-9e63-314c275afc12","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:26.931814Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:2415ac816adc4cf18c424ba4df92d2514b153b1777376e37033aed9ed06dfa62","observation_id":"4791b073-32e1-458b-a08a-3c84b0f8d35b","resolution":{"observed_at":"2026-08-06T19:53:38.225241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:37.798382Z","title":"Fruits-262 dataset: A dataset containing a vast majority of the popular and known fruits, 2021","venue":null,"work_id":"a5e2bcb2-3502-4556-b7bd-b3da1a51c132","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.059971Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:3ab8d26ba25a6d943dbaa4f4ad44007e310968b53d0db7a8d83e822e2a7a97df","observation_id":"ae1c6187-3828-44d9-8a00-8456315337df","resolution":{"observed_at":"2026-08-06T19:53:37.906137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:27.179810Z","title":"Using deep learning for image-based plant disease detection","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.179810Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:95886976af87f2a92052ef57aacfc04063580814bd2a2fdeca8f7d1f012c709a","observation_id":"b5be9c7a-0031-4bf5-a487-572ff06d40d1","resolution":{"observed_at":"2026-08-06T19:53:27.179810Z","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":"2026-08-06T19:53:37.470401Z","title":"Llava-chef: A multi- modal generative model for food recipes","venue":null,"work_id":"e6858083-c234-4428-b9a9-eba6cc9ca99e","year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.273778Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:d229c17390875f0c0a16ade514f5f281fb6c3dd6178eb69a38ce3ec9e1fc1ae0","observation_id":"ddfa4779-5c8c-4ba7-8508-a9fc71506a73","resolution":{"observed_at":"2026-08-06T19:53:37.628796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2403.05435","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:53:31.044489Z","title":"Omnicount: Multi-label object count- ing with semantic-geometric priors","venue":null,"work_id":"2f170f8f-94e4-4062-9e07-c5b229c76007","year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.379914Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:7c77ce987d1c667e372e86ffb6e0a9a331c43df1f512238ea8e3fd35a2be0fe5","observation_id":"bc9170a9-f3c8-44fd-b9d0-ff195124fb95","resolution":{"observed_at":"2026-08-06T19:53:31.131578Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:37.168320Z","title":"Terrace-based food counting and segmentation","venue":null,"work_id":"e61370ff-c958-424b-8af0-6a6783216823","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.517857Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:28f2d4ae234091a178880316d260b85fe84ef4fa0045e51bdc4bdf038619593b","observation_id":"049ee9d8-8b9c-40a1-b0f5-bc644c97a5c8","resolution":{"observed_at":"2026-08-06T19:53:37.312089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:36.942175Z","title":"Sibnet: Food instance counting and segmentation","venue":null,"work_id":"d65faa2d-3663-4bab-ae60-799688a5f296","year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.603573Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:ce8a9a1805742568ef9f1de4bfa0886ab4c2316096c10c6ac21cc31cd67fc308","observation_id":"80585f7e-3df5-4ad7-8ff0-6c3b10f081c3","resolution":{"observed_at":"2026-08-06T19:53:37.069751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:36.655799Z","title":"Honey dataset stan- dard using hyperspectral imaging for machine learning prob- lems","venue":null,"work_id":"24e95ad7-84ff-4fd7-a03f-0e3cae589503","year":2017},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.764729Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:26e49f2a3bd6e49170925992198a4136b0f5e1e5c656e43df91b31cadd5a489d","observation_id":"eb867692-bfd8-4f6e-8caf-25413ae47dee","resolution":{"observed_at":"2026-08-06T19:53:36.791037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:36.420627Z","title":"Uec-foodpix complete: A large-scale food image segmentation dataset","venue":null,"work_id":"3e792253-a2a5-4a03-9deb-4c540334df81","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.904403Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:395cbf61efe552ba739216e7981bac9293d5d28c3d7a2fa4a32fad2f184615b2","observation_id":"3ff5d256-b4b2-4a24-9452-d21d213f5753","resolution":{"observed_at":"2026-08-06T19:53:36.510028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:36.273000Z","title":"Foodd: food detection dataset for calorie mea- surement using food images","venue":null,"work_id":"92940efd-471a-4cc3-9260-74c03e7d88c0","year":2015},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:27.990438Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:d17a414bb3f65ca6a99e69c4bdf70bbdd51acfb904558cfb602a9f9845cf4dfa","observation_id":"7fcfcdcf-f1b4-4cf9-b033-2d757bfaa3dc","resolution":{"observed_at":"2026-08-06T19:53:36.345918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:36.060309Z","title":"Learning to count everything","venue":null,"work_id":"0a2bba5c-86f4-40f5-b4c4-40f06b8f6260","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.115644Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f60b601eaa439604238a9d7f84f353f80e83732f13104207bca0868f283a1e77","observation_id":"505413ff-667f-4a50-beac-fec92ff09a79","resolution":{"observed_at":"2026-08-06T19:53:36.194324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:35.850522Z","title":"Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy","venue":null,"work_id":"7e85e253-36e1-47f9-9539-93187b8f5a47","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.240031Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:2983ffa83bc549ef41e830cd8807356742de51b21a41d538b762ac207e60ded7","observation_id":"1b3757c7-869b-4e61-9e7d-5f390cb534d0","resolution":{"observed_at":"2026-08-06T19:53:35.961390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:35.659825Z","title":"Multi-task learn- ing for calorie prediction on a novel large-scale recipe dataset enriched with nutritional information","venue":null,"work_id":"3a05064b-fd76-4aa7-94d7-a8c006836599","year":2020},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.340251Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:90a5f5b79bde98a398e02d1cc28d9d96b2b571c33d83c21515d4494206f65ebc","observation_id":"a021bfa3-e0a4-4b9c-a568-14886c1a261b","resolution":{"observed_at":"2026-08-06T19:53:35.728195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:35.446760Z","title":"Represent, compare, and learn: A similarity-aware framework for class-agnostic counting","venue":null,"work_id":"e97a6280-325e-45cb-8df9-10d4f9bfa568","year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.459533Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:77691455b80291930cb50683b7a934ae8ebd49bd085c07481e181712fdcf7ec4","observation_id":"3b33dd40-68da-4aea-a358-0918a7107ddf","resolution":{"observed_at":"2026-08-06T19:53:35.574649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:35.227985Z","title":"Plantdoc: A dataset for visual plant disease detection","venue":null,"work_id":"613779b4-3b36-42c5-a57d-b48c848c0a0d","year":2020},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.573773Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f6136baad7902d0420343b3fcbb2112521c184e1ffc410f50cefc067ab85d04d","observation_id":"89722a38-d672-4e04-8177-9d8b45ce0b77","resolution":{"observed_at":"2026-08-06T19:53:35.323633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:28.672510Z","title":"The cropandweed dataset: A multi-modal learning approach for efficient crop and weed manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.672510Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:047f38702478c4ab4cd634b06a694ca23c13b784e918d11e86c132b43f452232","observation_id":"17a11e7b-2365-4dc8-9d78-6f6e871ccc3e","resolution":{"observed_at":"2026-08-06T19:53:28.672510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05619","last_updated":"2023-04-12T05:27:30Z","snapshot_observed_at":"2026-07-06T15:14:40.210547Z","submitted_at":"2023-04-12T05:27:30Z","title":"NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation","version":1},"cited_work":{"arxiv_id":"2304.05619","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.05619","snapshot_observed_at":"2026-08-06T19:53:30.800745Z","title":"NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation","venue":"cs.CV","work_id":"f6db4afc-ef47-4c81-aa45-f7d3e87d0138","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.785531Z"},"links":{"cited_paper":"/paper/2304.05619","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:e1374898865bbcc8e73022f9db8a1f8a743223466ad23886f1f07057f52848a4","observation_id":"9eaa3be0-5a8d-4cc0-af57-c31be66bebc7","resolution":{"observed_at":"2026-08-06T19:53:30.885920Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:34.988485Z","title":"Classification of biscuit defect states and foreign objects using cnn-based features","venue":null,"work_id":"81365123-a086-4c18-8e70-58fb58d26c7a","year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.895200Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f1e64e25d4e8601b7af34f7c95933808803890cd75a72a9f50bd855e76b4029e","observation_id":"b9550a4e-862b-4d03-a479-56fe48089bd0","resolution":{"observed_at":"2026-08-06T19:53:35.087350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:34.735592Z","title":"Nutrition5k: To- wards automatic nutritional understanding of generic food","venue":null,"work_id":"a6244a2c-b654-4617-bf43-bb4527e78f1c","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:28.987157Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:2f9d0b7ff972e196bedd5cde60ca9f5c932a022123cd2b0869ba031da6b0f3ff","observation_id":"355f910f-24d0-466b-af96-bdcb2cf9445e","resolution":{"observed_at":"2026-08-06T19:53:34.852539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:34.563407Z","title":"Rice seedling detection in uav images using transfer learning and machine learning","venue":null,"work_id":"6df6a385-0359-41bb-9314-dee43ea2f764","year":2022},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.088218Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:b852909c3fee2d1ebb95de8d947856be7a43a5cb9d35eca20715292380de7859","observation_id":"c0309ff0-86cf-4bde-9ae6-20c746f2497c","resolution":{"observed_at":"2026-08-06T19:53:34.661513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:34.331549Z","title":"Solov2: Dynamic and fast instance segmenta- tion","venue":null,"work_id":"842ca0b9-2419-42bf-81d0-6b01339bc1c2","year":2020},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.199151Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:fda2d1d56c70a95244de5b292a0e612b72423b72812c6b608d6c235f84a98677","observation_id":"de0a6269-a8f4-4808-a1f1-48d221ed31ec","resolution":{"observed_at":"2026-08-06T19:53:34.394415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:34.099760Z","title":"Multi-state ingredient recognition via adaptive multi-centric network","venue":null,"work_id":"0db36852-e9de-4fb8-83b5-418a3acfacc1","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.290818Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:a4e9bf99c904cedafd0d9c82f9283dfbbe02f8f70ece794c878a83b28a28570a","observation_id":"c7ded2d5-9f00-4262-b816-cf90a767323c","resolution":{"observed_at":"2026-08-06T19:53:34.229069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:33.897206Z","title":"Automatic counting of in situ rice seedlings from uav images based on a deep fully convolutional neural network","venue":null,"work_id":"96e44e9c-451d-474d-910e-09cc776598b0","year":2019},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.423316Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:597b14ddad55293be9ebc19f48f33ea2f11fcd8cc866785c5ebba47dd728be74","observation_id":"bdfc3e80-2140-4118-b0b3-b034fa791bcf","resolution":{"observed_at":"2026-08-06T19:53:34.027399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:33.627978Z","title":"A large-scale benchmark for food im- age segmentation","venue":null,"work_id":"edd316e6-82e2-4143-8bd3-6b07d2f70c09","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.547638Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:30567cfc33c83fffb8f8228518add13dc87df7505eed82c282c8bd9e374b1c6d","observation_id":"1d793bfb-7af9-4384-9797-6ceae18f0e74","resolution":{"observed_at":"2026-08-06T19:53:33.761871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:33.392337Z","title":"Hsifoodingr-64: A dataset for hyperspectral food-related studies and a benchmark method on food ingredient retrieval","venue":null,"work_id":"b663499d-2826-4c6d-b07d-253ec2517fa0","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.681511Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f610dd3f1962f9307196149ed15d4d79e51e995a58da82e362a57c81d259b88d","observation_id":"a2377af5-c08e-46bb-b898-1abb3dbe559e","resolution":{"observed_at":"2026-08-06T19:53:33.503989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:33.200346Z","title":"Multiple attentional pyra- mid networks for chinese herbal recognition","venue":null,"work_id":"8697d2fa-cc6c-4c6e-8130-75f9cfbc36f3","year":2021},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.819436Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:ba47c941d06b7d492af4c489e9769d4b2a0320b667f5843984299a770a53c3df","observation_id":"2fc314d1-32f5-4dbc-bc48-0b40635357c3","resolution":{"observed_at":"2026-08-06T19:53:33.312453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14991","last_updated":"2024-04-12T14:21:20Z","snapshot_observed_at":"2026-07-06T17:07:23.809593Z","submitted_at":"2023-12-22T11:56:22Z","title":"FoodLMM: A Versatile Food Assistant using Large Multi-modal Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.14991","snapshot_observed_at":"2026-08-06T19:53:29.966947Z","title":"Foodlmm: A versatile food assistant using large multi-modal model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:29.966947Z"},"links":{"cited_paper":"/paper/2312.14991","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:f5bb8ddc62bcb5a51fbac0108ac925ea80a5abb5c883234a8b8d4ccfe7921eb1","observation_id":"54e9e856-c769-4a4c-a3fa-02ba0df1f4d4","resolution":{"observed_at":"2026-08-06T19:53:29.966947Z","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":"2026-08-06T19:53:32.971552Z","title":"Fine-grained image classifi- cation by exploring bipartite-graph labels","venue":null,"work_id":"cb521a23-3f4e-4906-9f5a-c12992ce429f","year":2016},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.054060Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:329681c58580d060de86ded94d3409d8146002217acb3f7c356a0883c947d5a4","observation_id":"bdc040f6-15b7-47d0-8e33-c3ad6c81a8f3","resolution":{"observed_at":"2026-08-06T19:53:33.027010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10261","last_updated":"2024-06-11T01:27:00Z","snapshot_observed_at":"2026-08-05T16:40:22.949421Z","submitted_at":"2024-06-11T01:27:00Z","title":"FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination","version":1},"cited_work":{"arxiv_id":"2406.10261","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.10261","snapshot_observed_at":"2026-08-06T19:53:30.630031Z","title":"FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination","venue":"cs.CL","work_id":"85bf8f92-0519-423a-ba52-9e9c88017076","year":2024},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.110308Z"},"links":{"cited_paper":"/paper/2406.10261","citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:abb577a6a3b4a4b95fe8861a00f3859f20a72fa8f8030b878c41adbe1f7610b2","observation_id":"63074190-482e-4b65-af61-83c56394b3f4","resolution":{"observed_at":"2026-08-06T19:53:30.698067Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:32.691246Z","title":"Learn more for food recognition via progressive self-distillation","venue":null,"work_id":"a9566c8a-12a9-4b15-abb9-fdd2dfeb6710","year":2023},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.181170Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:a80c236f156935849084bfd7a50e9eb1246b0958aa424437eb7b2cbdef1ce1f1","observation_id":"eebb64af-2f74-4a5d-8395-05255c607a05","resolution":{"observed_at":"2026-08-06T19:53:32.796328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:32.489759Z","title":"It is worth noting that these categories do not represent all foods","venue":null,"work_id":"bded673e-47b0-4801-a78f-a7171308e815","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.299412Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:b4b791fe0f29aa3614f71a5bb76b640a9577cf23b5fcb3cc06fdfb3b3fe5a764","observation_id":"bba88002-894d-4fa0-b8cc-4480198e88ca","resolution":{"observed_at":"2026-08-06T19:53:32.611162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:32.264323Z","title":null,"venue":null,"work_id":"8fe9e76c-27cf-45b4-9890-560e0732fa79","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.385245Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:c12034741a59558be52e410c78e9002a0ffb411239fefd83ca167eecbfb42d77","observation_id":"8cc279fd-271d-41cc-9b8e-42cbb916967b","resolution":{"observed_at":"2026-08-06T19:53:32.362474Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:31.978356Z","title":"2), we have obtained spectral data with wavelength between 400 nm to 1020 nm shown in Table 8","venue":null,"work_id":"af69e3eb-d508-468f-aa98-df48edddd852","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.462315Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:24b7019f29ed52ac38d546c54c73d5d964fb530583426e023035e479fba9b244","observation_id":"10e8cd40-c7fe-4a6a-bb04-22de95b0e843","resolution":{"observed_at":"2026-08-06T19:53:32.141100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-08-06T19:53:31.773142Z","title":null,"venue":null,"work_id":"bcca0bd1-9dcd-4889-b9c4-c9f7bd654b24","year":null},"citing_paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T19:53:30.508953Z"},"links":{"citing_paper":"/paper/2507.04412"},"observation_digest":"sha256:da8da90b1cc6074851972f1b1fd36369fd12fdbdd7d5a77a98f0e9f4297572d7","observation_id":"566bf4cc-965e-4d73-8b88-6f96a0bcabf7","resolution":{"observed_at":"2026-08-06T19:53:31.851022Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.04412","last_updated":"2025-07-06T15:00:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T19:45:40.243918Z","submitted_at":"2025-07-06T15:00:21Z","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":5,"verified_fuzzy":50},"total_outbound_references":65},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2507.04412."}