AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding
AdaptToken uses an entropy-based mechanism to intelligently select the most relevant tokens from long videos for Multi-modal Large Language Models (MLLMs). This overcomes memory and context limitations, significantly improving MLLM efficiency and effectiveness for extended video understanding tasks.
4 Steps
- 1
Understand MLLM Video Processing Limitations: Recognize that traditional MLLM approaches struggle with long videos due to high memory costs and limited context windows, often processing only short, pre-defined clips.
- 2
Implement Entropy-Based Information Scoring: Develop or integrate a method to quantify the 'informativeness' (entropy) of individual tokens or frames within video segments. High entropy indicates more unique or critical information.
- 3
Apply Adaptive Cross-Clip Token Selection: Design an algorithm to compare and select the most informative (high-entropy) tokens not just within a single video segment, but across multiple, potentially disparate, video clips. Prioritize tokens that offer the most novel information.
- 4
Integrate Selected Tokens into MLLM Pipeline: Feed the adaptively selected, high-entropy tokens as input to your MLLM. This reduces the overall token count while retaining critical information, enabling the MLLM to process substantially longer videos more effectively.
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