{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ACI7VRQRAEVPXRYBATMPRRFFH2","short_pith_number":"pith:ACI7VRQR","schema_version":"1.0","canonical_sha256":"0091fac611012afbc70104d8f8c4a53e8c2e5216d4ee4ba2d8c6bb6e0049b64c","source":{"kind":"arxiv","id":"1908.01482","version":1},"attestation_state":"computed","paper":{"title":"Walking with MIND: Mental Imagery eNhanceD Embodied QA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Fei Wu, Juncheng Li, Siliang Tang, Yueting Zhuang","submitted_at":"2019-08-05T06:17:03Z","abstract_excerpt":"The EmbodiedQA is a task of training an embodied agent by intelligently navigating in a simulated environment and gathering visual information to answer questions. Existing approaches fail to explicitly model the mental imagery function of the agent, while the mental imagery is crucial to embodied cognition, and has a close relation to many high-level meta-skills such as generalization and interpretation. In this paper, we propose a novel Mental Imagery eNhanceD (MIND) module for the embodied agent, as well as a relevant deep reinforcement framework for training. The MIND module can not only m"},"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":"1908.01482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-05T06:17:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"20c4e37da6d5f35f88dc33fcf0cb837a1ee1e91fee41d4a0f194bb58978cbf47","abstract_canon_sha256":"319ee6b317b0d043b35490223a2307778adda9ef92e98b044e7517a8d68863dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:51:30.880592Z","signature_b64":"QxReBsZ1y5jGyvb4/QBsbTDJ5JCkXfonxDWz6XJ4bVWLjk3/XD2NVCF1zWO55lB3yhW3mmoP90uAUgbhog6BAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0091fac611012afbc70104d8f8c4a53e8c2e5216d4ee4ba2d8c6bb6e0049b64c","last_reissued_at":"2026-07-04T23:51:30.880230Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:51:30.880230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Walking with MIND: Mental Imagery eNhanceD Embodied QA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Fei Wu, Juncheng Li, Siliang Tang, Yueting Zhuang","submitted_at":"2019-08-05T06:17:03Z","abstract_excerpt":"The EmbodiedQA is a task of training an embodied agent by intelligently navigating in a simulated environment and gathering visual information to answer questions. Existing approaches fail to explicitly model the mental imagery function of the agent, while the mental imagery is crucial to embodied cognition, and has a close relation to many high-level meta-skills such as generalization and interpretation. In this paper, we propose a novel Mental Imagery eNhanceD (MIND) module for the embodied agent, as well as a relevant deep reinforcement framework for training. The MIND module can not only m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01482","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/1908.01482/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":"1908.01482","created_at":"2026-07-04T23:51:30.880286+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.01482v1","created_at":"2026-07-04T23:51:30.880286+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01482","created_at":"2026-07-04T23:51:30.880286+00:00"},{"alias_kind":"pith_short_12","alias_value":"ACI7VRQRAEVP","created_at":"2026-07-04T23:51:30.880286+00:00"},{"alias_kind":"pith_short_16","alias_value":"ACI7VRQRAEVPXRYB","created_at":"2026-07-04T23:51:30.880286+00:00"},{"alias_kind":"pith_short_8","alias_value":"ACI7VRQR","created_at":"2026-07-04T23:51:30.880286+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/ACI7VRQRAEVPXRYBATMPRRFFH2","json":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2.json","graph_json":"https://pith.science/api/pith-number/ACI7VRQRAEVPXRYBATMPRRFFH2/graph.json","events_json":"https://pith.science/api/pith-number/ACI7VRQRAEVPXRYBATMPRRFFH2/events.json","paper":"https://pith.science/paper/ACI7VRQR"},"agent_actions":{"view_html":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2","download_json":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2.json","view_paper":"https://pith.science/paper/ACI7VRQR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.01482&json=true","fetch_graph":"https://pith.science/api/pith-number/ACI7VRQRAEVPXRYBATMPRRFFH2/graph.json","fetch_events":"https://pith.science/api/pith-number/ACI7VRQRAEVPXRYBATMPRRFFH2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2/action/storage_attestation","attest_author":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2/action/author_attestation","sign_citation":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2/action/citation_signature","submit_replication":"https://pith.science/pith/ACI7VRQRAEVPXRYBATMPRRFFH2/action/replication_record"}},"created_at":"2026-07-04T23:51:30.880286+00:00","updated_at":"2026-07-04T23:51:30.880286+00:00"}