{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RROOVISJRHUQNSKAWCE62RMR5P","short_pith_number":"pith:RROOVISJ","schema_version":"1.0","canonical_sha256":"8c5ceaa24989e906c940b089ed4591ebeedec19c927ec445f05d7e86398369f1","source":{"kind":"arxiv","id":"2505.08747","version":1},"attestation_state":"computed","paper":{"title":"Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bin Zhu, Chong-Wah Ngo, Ee-Peng Lim, Huiyan Qi, Jingjing Chen","submitted_at":"2025-05-13T17:01:21Z","abstract_excerpt":"Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF$^2$) method to enhance nutrition estimation by integrating"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.08747","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-13T17:01:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8b46bc63c40f322c5ea36fc6699fb29efe26e8feb3b3690156a1abb08e77b1f3","abstract_canon_sha256":"f45e9f606175b8b6cdc4b07dbbacb983f2c672dd79c4d373a763c37a9d2b25d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:35.830466Z","signature_b64":"s7ijXwRVi328RViDLRxHEYvPid9Y/JfCzdj63hK/3fJZ22DB4Lf4vrQbv+jSnozReUNuxegG00unZuL7wBFACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c5ceaa24989e906c940b089ed4591ebeedec19c927ec445f05d7e86398369f1","last_reissued_at":"2026-07-05T11:02:35.829982Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:35.829982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bin Zhu, Chong-Wah Ngo, Ee-Peng Lim, Huiyan Qi, Jingjing Chen","submitted_at":"2025-05-13T17:01:21Z","abstract_excerpt":"Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF$^2$) method to enhance nutrition estimation by integrating"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08747","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.08747/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.08747","created_at":"2026-07-05T11:02:35.830040+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.08747v1","created_at":"2026-07-05T11:02:35.830040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08747","created_at":"2026-07-05T11:02:35.830040+00:00"},{"alias_kind":"pith_short_12","alias_value":"RROOVISJRHUQ","created_at":"2026-07-05T11:02:35.830040+00:00"},{"alias_kind":"pith_short_16","alias_value":"RROOVISJRHUQNSKA","created_at":"2026-07-05T11:02:35.830040+00:00"},{"alias_kind":"pith_short_8","alias_value":"RROOVISJ","created_at":"2026-07-05T11:02:35.830040+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P","json":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P.json","graph_json":"https://pith.science/api/pith-number/RROOVISJRHUQNSKAWCE62RMR5P/graph.json","events_json":"https://pith.science/api/pith-number/RROOVISJRHUQNSKAWCE62RMR5P/events.json","paper":"https://pith.science/paper/RROOVISJ"},"agent_actions":{"view_html":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P","download_json":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P.json","view_paper":"https://pith.science/paper/RROOVISJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.08747&json=true","fetch_graph":"https://pith.science/api/pith-number/RROOVISJRHUQNSKAWCE62RMR5P/graph.json","fetch_events":"https://pith.science/api/pith-number/RROOVISJRHUQNSKAWCE62RMR5P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P/action/storage_attestation","attest_author":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P/action/author_attestation","sign_citation":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P/action/citation_signature","submit_replication":"https://pith.science/pith/RROOVISJRHUQNSKAWCE62RMR5P/action/replication_record"}},"created_at":"2026-07-05T11:02:35.830040+00:00","updated_at":"2026-07-05T11:02:35.830040+00:00"}