AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding
📰 ArXiv cs.AI
AdaptToken is a framework for adaptive token selection in MLLM long video understanding, using entropy-based methods to improve efficiency
Action Steps
- Identify the limitations of current MLLM approaches to long video understanding, such as high memory costs and context-length limits
- Develop an entropy-based adaptive token selection method to compare relevance across distant video clips
- Implement a stopping criterion to halt processing once sufficient evidence has been gathered
- Evaluate the effectiveness of AdaptToken in improving the efficiency of MLLM long video understanding
Who Needs to Know This
This research benefits AI engineers and ML researchers working on multimodal large language models, as it provides a novel approach to improve the efficiency of long video understanding tasks
Key Insight
💡 Entropy-based adaptive token selection can improve the efficiency of MLLM long video understanding by reducing memory costs and context-length limits
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📹🤖 AdaptToken: entropy-based adaptive token selection for efficient MLLM long video understanding
Key Takeaways
AdaptToken is a framework for adaptive token selection in MLLM long video understanding, using entropy-based methods to improve efficiency
Full Article
Title: AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding
Abstract:
arXiv:2603.28696v1 Announce Type: cross Abstract: Long video understanding remains challenging for Multi-modal Large Language Models (MLLMs) due to high memory costs and context-length limits. Prior approaches mitigate this by scoring and selecting frames/tokens within short clips, but they lack a principled mechanism to (i) compare relevance across distant video clips and (ii) stop processing once sufficient evidence has been gathered. We propose AdaptToken, a training-free framework that turns
Abstract:
arXiv:2603.28696v1 Announce Type: cross Abstract: Long video understanding remains challenging for Multi-modal Large Language Models (MLLMs) due to high memory costs and context-length limits. Prior approaches mitigate this by scoring and selecting frames/tokens within short clips, but they lack a principled mechanism to (i) compare relevance across distant video clips and (ii) stop processing once sufficient evidence has been gathered. We propose AdaptToken, a training-free framework that turns
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