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FastVLA: Task-conditioned and temporally adaptive visual token compression for vision-language-action models

FastVLA proposes a training-free, task-aware, and temporally adaptive visual token compression framework to improve the closed-loop inference efficiency of vision-language-action (VLA) models. It scores tokens via task-to-vision attention and adaptively sets the compression rate based on the uniformity of the importance distribution, calibrated using an inverse Simpson index and an online sliding window.

Part of the event: FastVLA Visual Token Compression Framework Accepted to WACV 2027

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