arXiv Computer Vision
Frame Differential On-Policy Self-Distillation for Video Reasoning
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Video-OPSD: Exploiting Privileged Visual Evidence for On-Policy Self-Distillation in Video Large Language Models
Video-OPSD introduces a post‑training framework for Video Large Language Models that leverages privileged visual evidence to enhance on‑policy self‑distillation. The method constructs a self‑teacher conditioned only on annotated evidence frames, while the student processes the full video, allowing the teacher to provide more focused supervision. Additionally, an evidence‑guided token optimization scheme weights distillation based on each token’s reliance on privileged evidence, improving perceptually grounded reasoning. Experiments demonstrate consistent gains over standard OPSD and comparable performance to GRPO with less training time.
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VISD: Enhancing Video Reasoning via Structured Self-Distillation
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arXiv AI
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VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting
VisionCoach is an input‑adaptive reinforcement learning framework that enhances spatio‑temporal grounding in video reasoning by using visual prompting during training. The system selectively applies visual prompts to challenging inputs, amplifying question‑relevant evidence and suppressing distractors, and then internalizes these improvements through self‑distillation so that inference can be performed on raw videos without prompts. Experiments on multiple benchmarks (V‑STAR, VideoMME, World‑Sense, VideoMMMU, PerceptionTest, and Charades‑STA) show that VisionCoach achieves state‑of‑the‑art performance while maintaining a single efficient inference pathway.
arXiv Computer Vision
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Video Evidence to Reasoning Efficient Video Understanding via Explicit Evidence Grounding
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arXiv AI
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Reflect-R1: Evidence-Driven Reflection for Self-Correction in Long Video Understanding
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Hugging Face Trending Papers
Aug 26
Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding
Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions from frames sampled across the full video. However...