{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5JK44DJAGE2THAEXQG6ZKVBWDR","short_pith_number":"pith:5JK44DJA","schema_version":"1.0","canonical_sha256":"ea55ce0d20313533809781bd9554361c5eeed242522aa4198001555713373d46","source":{"kind":"arxiv","id":"1705.00601","version":2},"attestation_state":"computed","paper":{"title":"The Promise of Premise: Harnessing Question Premises in Visual Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Akrit Mohapatra, Aroma Mahendru, Dhruv Batra, Stefan Lee, Viraj Prabhu","submitted_at":"2017-05-01T17:41:37Z","abstract_excerpt":"In this paper, we make a simple observation that questions about images often contain premises - objects and relationships implied by the question - and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is "},"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":"1705.00601","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-01T17:41:37Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"806a699b99ae8823fae88e24c2dda01292ddd5987c135a6f87728ff520685bfc","abstract_canon_sha256":"10fc13abd0b5a616f78ac00b042a4ab9d38c770d4063ce0f96392eb6356d4db4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:52.476349Z","signature_b64":"YGVsUTBosagNX1V/r1V+0SpNkYpmhrRhamf9INnDMEYw8AG45agQqUmUAlQ5jgXUc64TAobC1vGXLwWV0u8CBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea55ce0d20313533809781bd9554361c5eeed242522aa4198001555713373d46","last_reissued_at":"2026-05-18T00:37:52.475805Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:52.475805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Promise of Premise: Harnessing Question Premises in Visual Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Akrit Mohapatra, Aroma Mahendru, Dhruv Batra, Stefan Lee, Viraj Prabhu","submitted_at":"2017-05-01T17:41:37Z","abstract_excerpt":"In this paper, we make a simple observation that questions about images often contain premises - objects and relationships implied by the question - and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.00601","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1705.00601","created_at":"2026-05-18T00:37:52.475876+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.00601v2","created_at":"2026-05-18T00:37:52.475876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.00601","created_at":"2026-05-18T00:37:52.475876+00:00"},{"alias_kind":"pith_short_12","alias_value":"5JK44DJAGE2T","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"5JK44DJAGE2THAEX","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"5JK44DJA","created_at":"2026-05-18T12:31:00.734936+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/5JK44DJAGE2THAEXQG6ZKVBWDR","json":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR.json","graph_json":"https://pith.science/api/pith-number/5JK44DJAGE2THAEXQG6ZKVBWDR/graph.json","events_json":"https://pith.science/api/pith-number/5JK44DJAGE2THAEXQG6ZKVBWDR/events.json","paper":"https://pith.science/paper/5JK44DJA"},"agent_actions":{"view_html":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR","download_json":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR.json","view_paper":"https://pith.science/paper/5JK44DJA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.00601&json=true","fetch_graph":"https://pith.science/api/pith-number/5JK44DJAGE2THAEXQG6ZKVBWDR/graph.json","fetch_events":"https://pith.science/api/pith-number/5JK44DJAGE2THAEXQG6ZKVBWDR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR/action/storage_attestation","attest_author":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR/action/author_attestation","sign_citation":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR/action/citation_signature","submit_replication":"https://pith.science/pith/5JK44DJAGE2THAEXQG6ZKVBWDR/action/replication_record"}},"created_at":"2026-05-18T00:37:52.475876+00:00","updated_at":"2026-05-18T00:37:52.475876+00:00"}