{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IUXYGNYWHVZMCDFSGC3FMPRMM7","short_pith_number":"pith:IUXYGNYW","schema_version":"1.0","canonical_sha256":"452f8337163d72c10cb230b6563e2c67d6aee88b03595c40c0d6f3600844b886","source":{"kind":"arxiv","id":"2306.05424","version":2},"attestation_state":"computed","paper":{"title":"Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Video-ChatGPT combines a video-adapted visual encoder with a large language model to support detailed conversations about video content.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fahad Shahbaz Khan, Hanoona Rasheed, Muhammad Maaz, Salman Khan","submitted_at":"2023-06-08T17:59:56Z","abstract_excerpt":"Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation models, this work addresses the under-explored field of \\emph{video-based conversation} by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with an LLM. The resulting model is capable of understanding and generating detailed conversations about videos. We introduce a new dataset of 100,000 video-instruction pairs used to train Video-ChatGPT acquired via manual and semi-a"},"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":true},"canonical_record":{"source":{"id":"2306.05424","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-08T17:59:56Z","cross_cats_sorted":[],"title_canon_sha256":"469c0d15adbd96a58fa5881c8367f13e8fde0ef3d902a0e1c26f3d1b7edc308a","abstract_canon_sha256":"5cb9fcb7415928f94a879a2872bd1094e96ff9a4802d49f2bb84d86f1481e1ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:53.670969Z","signature_b64":"VkzuK/WUbbHThUm2PH33A/HU+eJ54VW7IFbqnhItb9trEBRyFkrZzv4hBKgfhd9sGaFoB1T/L7vPEk6pbkEQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"452f8337163d72c10cb230b6563e2c67d6aee88b03595c40c0d6f3600844b886","last_reissued_at":"2026-05-17T23:38:53.670465Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:53.670465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Video-ChatGPT combines a video-adapted visual encoder with a large language model to support detailed conversations about video content.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fahad Shahbaz Khan, Hanoona Rasheed, Muhammad Maaz, Salman Khan","submitted_at":"2023-06-08T17:59:56Z","abstract_excerpt":"Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation models, this work addresses the under-explored field of \\emph{video-based conversation} by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with an LLM. The resulting model is capable of understanding and generating detailed conversations about videos. We introduce a new dataset of 100,000 video-instruction pairs used to train Video-ChatGPT acquired via manual and semi-a"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The resulting model is capable of understanding and generating detailed conversations about videos.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The semi-automated pipeline for creating the 100,000 video-instruction pairs produces sufficiently clean training data without label noise that would degrade the model's ability to generate accurate conversations.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Video-ChatGPT is a multimodal model that combines a video visual encoder with an LLM to understand and generate conversations about videos, trained on a new dataset of 100,000 video-instruction pairs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Video-ChatGPT combines a video-adapted visual encoder with a large language model to support detailed conversations about video content.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"533f3463748ac8910ef18fcb487e600635e42d9003b546018bbba6b1a4d37d38"},"source":{"id":"2306.05424","kind":"arxiv","version":2},"verdict":{"id":"a1200b5c-d98e-43aa-ac40-21ef3e152a62","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:29:56.240351Z","strongest_claim":"The resulting model is capable of understanding and generating detailed conversations about videos.","one_line_summary":"Video-ChatGPT is a multimodal model that combines a video visual encoder with an LLM to understand and generate conversations about videos, trained on a new dataset of 100,000 video-instruction pairs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The semi-automated pipeline for creating the 100,000 video-instruction pairs produces sufficiently clean training data without label noise that would degrade the model's ability to generate accurate conversations.","pith_extraction_headline":"Video-ChatGPT combines a video-adapted visual encoder with a large language model to support detailed conversations about video content."},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":3,"snapshot_sha256":"a45098cacb1b8d38046a9f62e97c2526b9000811dbcbe5618d22fde14339f022"},"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":"2306.05424","created_at":"2026-05-17T23:38:53.670550+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.05424v2","created_at":"2026-05-17T23:38:53.670550+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05424","created_at":"2026-05-17T23:38:53.670550+00:00"},{"alias_kind":"pith_short_12","alias_value":"IUXYGNYWHVZM","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"IUXYGNYWHVZMCDFS","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"IUXYGNYW","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":56,"internal_anchor_count":56,"sample":[{"citing_arxiv_id":"2504.09583","citing_title":"AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2407.08101","citing_title":"What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction","ref_index":42,"is_internal_anchor":true},{"citing_arxiv_id":"2411.02327","citing_title":"PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2412.02930","citing_title":"TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2501.05067","citing_title":"LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding","ref_index":48,"is_internal_anchor":true},{"citing_arxiv_id":"2502.02452","citing_title":"Personalization Toolkit: Training Free Personalization of Large Vision Language Models","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2503.09158","citing_title":"FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2505.15269","citing_title":"LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2605.22269","citing_title":"MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question-Answering","ref_index":26,"is_internal_anchor":true},{"citing_arxiv_id":"2512.15693","citing_title":"Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning","ref_index":40,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19950","citing_title":"AffectVerse: Emotional World Models for Multimodal Affective Computing","ref_index":27,"is_internal_anchor":true},{"citing_arxiv_id":"2408.04840","citing_title":"mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models","ref_index":238,"is_internal_anchor":true},{"citing_arxiv_id":"2307.06435","citing_title":"A Comprehensive Overview of Large Language Models","ref_index":273,"is_internal_anchor":true},{"citing_arxiv_id":"2505.23617","citing_title":"One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2406.08035","citing_title":"LVBench: An Extreme Long Video Understanding Benchmark","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2506.18962","citing_title":"UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding","ref_index":46,"is_internal_anchor":true},{"citing_arxiv_id":"2508.05269","citing_title":"B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2509.15602","citing_title":"TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2509.24943","citing_title":"Perceive, Verify and Understand Long Video: Multi-Granular Perception and Active Verification via Interactive Agents","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2311.04257","citing_title":"mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration","ref_index":42,"is_internal_anchor":true},{"citing_arxiv_id":"2310.13289","citing_title":"SALMONN: Towards Generic Hearing Abilities for Large Language Models","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2311.17005","citing_title":"MVBench: A Comprehensive Multi-modal Video Understanding Benchmark","ref_index":51,"is_internal_anchor":true},{"citing_arxiv_id":"2407.03320","citing_title":"InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output","ref_index":105,"is_internal_anchor":true},{"citing_arxiv_id":"2505.21374","citing_title":"Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning?","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2403.00476","citing_title":"TempCompass: Do Video LLMs Really Understand Videos?","ref_index":109,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":3,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7","json":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7.json","graph_json":"https://pith.science/api/pith-number/IUXYGNYWHVZMCDFSGC3FMPRMM7/graph.json","events_json":"https://pith.science/api/pith-number/IUXYGNYWHVZMCDFSGC3FMPRMM7/events.json","paper":"https://pith.science/paper/IUXYGNYW"},"agent_actions":{"view_html":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7","download_json":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7.json","view_paper":"https://pith.science/paper/IUXYGNYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.05424&json=true","fetch_graph":"https://pith.science/api/pith-number/IUXYGNYWHVZMCDFSGC3FMPRMM7/graph.json","fetch_events":"https://pith.science/api/pith-number/IUXYGNYWHVZMCDFSGC3FMPRMM7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7/action/storage_attestation","attest_author":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7/action/author_attestation","sign_citation":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7/action/citation_signature","submit_replication":"https://pith.science/pith/IUXYGNYWHVZMCDFSGC3FMPRMM7/action/replication_record"}},"created_at":"2026-05-17T23:38:53.670550+00:00","updated_at":"2026-05-17T23:38:53.670550+00:00"}