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LookThere! Sparse Vision by Reinforced Selection framework reduces computation

Researchers have developed LookThere, a novel framework that uses reinforcement learning to enable vision transformers to process only the most relevant image tokens. This approach significantly reduces computational load by training a separate input selector and representation extractor, allowing the model to learn where to focus and what to see without relying on heuristics. LookThere demonstrates impressive efficiency, maintaining accuracy with as little as 0.2% of input tokens, and shows strong generalization across various computer vision tasks including classification, segmentation, and regression. AI

IMPACT Enables significant computational savings in vision models by intelligently selecting relevant image tokens, potentially accelerating inference for high-resolution tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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LookThere! Sparse Vision by Reinforced Selection framework reduces computation

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The cluster contains an academic paper detailing a new method for computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sreehari Rammohan, Yousef Yassin, Anthony Fuller, Junfeng Wen, Carl Vondrick, Evan Shelhamer ·

    LookThere! Sparse Vision by Reinforced Selection

    arXiv:2609.04698v1 Announce Type: cross Abstract: Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive computation methods accelerate inference by choosing which tokens to process, b…