[Paper] Unified Spatio-Temporal Token Scoring for Efficient Video VLMs

Summary

This paper proposes a unified spatio-temporal token scoring method to significantly improve the computational efficiency of video Vision-Language Models (VLMs). It tackles the challenge of temporal redundancy in video data by enhancing token pruning, a critical technique for reducing processing load. Unlike previous methods that prune tokens either solely within the Vision Transformer for unimodal tasks or only within the Language Model, this approach offers a more integrated solution for video VLMs.

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