EventLens: Event-Structure Reinforcement Learning for Video Understanding
To address this, we propose EventLens, a vision-centric reinforcement learning framework that learns video understanding through event-structure recovery.
ProofPaper ↗
Key points
- Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure that makes a video coherent.
- We study structured temporal understanding as the ability to recover event structure from visual evidence, including event boundaries, single-event progression, and multi-event relations.
- We argue that this gap arises from a mismatch between current post-training objectives and temporal reasoning, where caption-style supervision allows models to rely on language shortcuts rather than visual temporal grounding.
- Instead of reconstructing captions, EventLens trains models to recover temporal structure under controlled perturbations, such as altered temporal granularity, reversed progression, and shuffled event order.
Sources (2)
- [1]EventLens: Event-Structure Reinforcement Learning for Video UnderstandingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:46 AM
To address this, we propose EventLens, a vision-centric reinforcement learning framework that learns video understanding through event-structure recovery.
Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure that makes a video coherent.
- [2]EventLens: Event-Structure Reinforcement Learning for Video UnderstandingHugging Face Daily Papers · Oct 12, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Before this
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