{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QNGCMLLLRZ5LU7WPUYTYKHJAOM","short_pith_number":"pith:QNGCMLLL","schema_version":"1.0","canonical_sha256":"834c262d6b8e7aba7ecfa627851d20733e52471a28c0b0cc2bc1234e90806fd3","source":{"kind":"arxiv","id":"2406.01938","version":1},"attestation_state":"computed","paper":{"title":"Nutrition Estimation for Dietary Management: A Transformer Approach with Depth Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Aik Beng Ng, Simon See, Wei Zhang, Zhengkui Wang, Zhengyi Kwan","submitted_at":"2024-06-04T03:45:08Z","abstract_excerpt":"Nutrition estimation is crucial for effective dietary management and overall health and well-being. Existing methods often struggle with sub-optimal accuracy and can be time-consuming. In this paper, we propose NuNet, a transformer-based network designed for nutrition estimation that utilizes both RGB and depth information from food images. We have designed and implemented a multi-scale encoder and decoder, along with two types of feature fusion modules, specialized for estimating five nutritional factors. These modules effectively balance the efficiency and effectiveness of feature extraction"},"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":"2406.01938","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T03:45:08Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"68e57d2674b0f0db737243059c76b69ff498e5f83dbbe566a226e506d0b74266","abstract_canon_sha256":"3d7308d484d3a35f266738d0c7c14363da0b859ff71320bec5987360fca5e0e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:11.895430Z","signature_b64":"qTqsoVZXIfy98VbrbdHtY83l0Dgmz+wdgjEFMj5uSkMVq9A2H1Q3Z4DKTdhGKPHXHxxg1t1f6jKSUcmokAM+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"834c262d6b8e7aba7ecfa627851d20733e52471a28c0b0cc2bc1234e90806fd3","last_reissued_at":"2026-07-05T08:27:11.895054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:11.895054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nutrition Estimation for Dietary Management: A Transformer Approach with Depth Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Aik Beng Ng, Simon See, Wei Zhang, Zhengkui Wang, Zhengyi Kwan","submitted_at":"2024-06-04T03:45:08Z","abstract_excerpt":"Nutrition estimation is crucial for effective dietary management and overall health and well-being. Existing methods often struggle with sub-optimal accuracy and can be time-consuming. In this paper, we propose NuNet, a transformer-based network designed for nutrition estimation that utilizes both RGB and depth information from food images. We have designed and implemented a multi-scale encoder and decoder, along with two types of feature fusion modules, specialized for estimating five nutritional factors. These modules effectively balance the efficiency and effectiveness of feature extraction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01938","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/2406.01938/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":"2406.01938","created_at":"2026-07-05T08:27:11.895108+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01938v1","created_at":"2026-07-05T08:27:11.895108+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01938","created_at":"2026-07-05T08:27:11.895108+00:00"},{"alias_kind":"pith_short_12","alias_value":"QNGCMLLLRZ5L","created_at":"2026-07-05T08:27:11.895108+00:00"},{"alias_kind":"pith_short_16","alias_value":"QNGCMLLLRZ5LU7WP","created_at":"2026-07-05T08:27:11.895108+00:00"},{"alias_kind":"pith_short_8","alias_value":"QNGCMLLL","created_at":"2026-07-05T08:27:11.895108+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.04997","citing_title":"GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM","json":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM.json","graph_json":"https://pith.science/api/pith-number/QNGCMLLLRZ5LU7WPUYTYKHJAOM/graph.json","events_json":"https://pith.science/api/pith-number/QNGCMLLLRZ5LU7WPUYTYKHJAOM/events.json","paper":"https://pith.science/paper/QNGCMLLL"},"agent_actions":{"view_html":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM","download_json":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM.json","view_paper":"https://pith.science/paper/QNGCMLLL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01938&json=true","fetch_graph":"https://pith.science/api/pith-number/QNGCMLLLRZ5LU7WPUYTYKHJAOM/graph.json","fetch_events":"https://pith.science/api/pith-number/QNGCMLLLRZ5LU7WPUYTYKHJAOM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM/action/storage_attestation","attest_author":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM/action/author_attestation","sign_citation":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM/action/citation_signature","submit_replication":"https://pith.science/pith/QNGCMLLLRZ5LU7WPUYTYKHJAOM/action/replication_record"}},"created_at":"2026-07-05T08:27:11.895108+00:00","updated_at":"2026-07-05T08:27:11.895108+00:00"}