{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NHB7VJIDSQ67UJT7OF22QASZK2","short_pith_number":"pith:NHB7VJID","schema_version":"1.0","canonical_sha256":"69c3faa503943dfa267f7175a8025956b8031e0877aba37d106c95e02e68ecbb","source":{"kind":"arxiv","id":"2607.23921","version":1},"attestation_state":"computed","paper":{"title":"DDVT: Dynamic Dual-level Vision Transformer Fusion Network for Answer Grounding in Visual Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongsheng Zhou, Wanshu Fan, Xiangyu Li, Xin Yang, Yue Zhang","submitted_at":"2026-07-27T01:26:15Z","abstract_excerpt":"Answer grounding in visual question answering aims to locate the region from a given natural language question associated with the visual content of an image, which has garnered significant attention due to its practical applications. In this paper, we introduce the Dynamic Dual-level Vision Transformer Fusion Network (DDVT) for answer grounding in visual question answering. Specifically, we propose a question-guided dynamic regional-level module (QGDR) that combines complementary image context through ROI Align and text content, enabling precise localization of text-related visual content. Mo"},"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":"2607.23921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-27T01:26:15Z","cross_cats_sorted":[],"title_canon_sha256":"97e0f468f4f942b6521d28103a92a31975310167b0670f5f1b89f3ee6574c7fc","abstract_canon_sha256":"10c8285c60c568d9e484617cef502bff545d0ce0387da9516ef5eba9b86e9e15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:23:17.874104Z","signature_b64":"tDoCRwFHtI9rtJgmj7MRW60S4BvXByqDz2q9Mk8dWBH/2q0pW03+Pa6fnSXLav+ivsY8mmQXrgER3cc22s3BAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69c3faa503943dfa267f7175a8025956b8031e0877aba37d106c95e02e68ecbb","last_reissued_at":"2026-07-28T01:23:17.873273Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:23:17.873273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DDVT: Dynamic Dual-level Vision Transformer Fusion Network for Answer Grounding in Visual Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongsheng Zhou, Wanshu Fan, Xiangyu Li, Xin Yang, Yue Zhang","submitted_at":"2026-07-27T01:26:15Z","abstract_excerpt":"Answer grounding in visual question answering aims to locate the region from a given natural language question associated with the visual content of an image, which has garnered significant attention due to its practical applications. In this paper, we introduce the Dynamic Dual-level Vision Transformer Fusion Network (DDVT) for answer grounding in visual question answering. Specifically, we propose a question-guided dynamic regional-level module (QGDR) that combines complementary image context through ROI Align and text content, enabling precise localization of text-related visual content. Mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.23921","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/2607.23921/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":"2607.23921","created_at":"2026-07-28T01:23:17.873703+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.23921v1","created_at":"2026-07-28T01:23:17.873703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.23921","created_at":"2026-07-28T01:23:17.873703+00:00"},{"alias_kind":"pith_short_12","alias_value":"NHB7VJIDSQ67","created_at":"2026-07-28T01:23:17.873703+00:00"},{"alias_kind":"pith_short_16","alias_value":"NHB7VJIDSQ67UJT7","created_at":"2026-07-28T01:23:17.873703+00:00"},{"alias_kind":"pith_short_8","alias_value":"NHB7VJID","created_at":"2026-07-28T01:23:17.873703+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/NHB7VJIDSQ67UJT7OF22QASZK2","json":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2.json","graph_json":"https://pith.science/api/pith-number/NHB7VJIDSQ67UJT7OF22QASZK2/graph.json","events_json":"https://pith.science/api/pith-number/NHB7VJIDSQ67UJT7OF22QASZK2/events.json","paper":"https://pith.science/paper/NHB7VJID"},"agent_actions":{"view_html":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2","download_json":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2.json","view_paper":"https://pith.science/paper/NHB7VJID","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.23921&json=true","fetch_graph":"https://pith.science/api/pith-number/NHB7VJIDSQ67UJT7OF22QASZK2/graph.json","fetch_events":"https://pith.science/api/pith-number/NHB7VJIDSQ67UJT7OF22QASZK2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2/action/storage_attestation","attest_author":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2/action/author_attestation","sign_citation":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2/action/citation_signature","submit_replication":"https://pith.science/pith/NHB7VJIDSQ67UJT7OF22QASZK2/action/replication_record"}},"created_at":"2026-07-28T01:23:17.873703+00:00","updated_at":"2026-07-28T01:23:17.873703+00:00"}