ReQuest introduces an uncertainty-driven question-adaptive keyframe selector with rethinking routing and adaptive NMS that boosts long-form video QA accuracy on Video-MME, MLVU, and LongVideoBench without fine-tuning the base MLLM.
Mm-vid: Advancing video understanding with gpt-4v (ision)
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
Soap2Soap uses a multi-agent system with dual-bridge consistency via JSON screenplays and visual anchors plus batch keyframe generation to achieve better long-term consistency in cinematic video remaking than commercial APIs.
READ is the first reinforcement-learning framework for training audio-description generators, using sequence-level rewards for reference match, length, format, and context-aware coherence.
StoryVideoQA provides the largest auto-generated deep video understanding dataset to date with 363K QAs across TV and movies, paired with the PlotTree agent for hierarchical plot-based reasoning that existing VideoQA models struggle to match.
VideoStir introduces a spatio-temporal graph-based structure and intent-aware retrieval for long-video RAG, achieving competitive performance with SOTA methods via a new IR-600K dataset.
AI drafts for audio description reduce editing time and cognitive load only when they exceed a content-dependent quality threshold, unlike unguided baseline drafts.
citing papers explorer
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ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA
ReQuest introduces an uncertainty-driven question-adaptive keyframe selector with rethinking routing and adaptive NMS that boosts long-form video QA accuracy on Video-MME, MLVU, and LongVideoBench without fine-tuning the base MLLM.
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Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration
Soap2Soap uses a multi-agent system with dual-bridge consistency via JSON screenplays and visual anchors plus batch keyframe generation to achieve better long-term consistency in cinematic video remaking than commercial APIs.
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READ More than What You See: Reinforcement Learning for Accurate and Coherent Audio Description Generations
READ is the first reinforcement-learning framework for training audio-description generators, using sequence-level rewards for reference match, length, format, and context-aware coherence.
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StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset
StoryVideoQA provides the largest auto-generated deep video understanding dataset to date with 363K QAs across TV and movies, paired with the PlotTree agent for hierarchical plot-based reasoning that existing VideoQA models struggle to match.
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VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG
VideoStir introduces a spatio-temporal graph-based structure and intent-aware retrieval for long-video RAG, achieving competitive performance with SOTA methods via a new IR-600K dataset.
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Making AI Drafts Count: A Quality Threshold in Audio Description Workflows
AI drafts for audio description reduce editing time and cognitive load only when they exceed a content-dependent quality threshold, unlike unguided baseline drafts.