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title
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Selective Visual Representations Improve Convergence and Generalization for Embodied AI
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2026-02-23 05:28:35
raw text
Embodied AI Codebook Ainaz Eftekhar* 1,2 , Kuo-Hao Zeng* 2 , Jiafei Duan 1,2 , Ali Farhadi 1, 2 , Ani Kembhavi 1, 2 , Ranjay Krishna 1,2 1 University of Washington, 2 Allen Institute for Artifical Intelligence *Equal Contribution ICLR , 2024 [Spotlight] ArXiv Code Slides Inspired by selective attention in humans—the process through which people filter their perception based on their experiences, knowledge, and the task at hand—we introduce a parameter-efficient approach to filter visual stimuli for Embodied-AI. Abstract Embodied-AI models often employ off the shelf vision backbones like CLIP to encode their visual observations. Although such general purpose representations encode rich syntactic and semantic information about the scene, much of this information is often irrelevant to the specific task at hand. This introduces noise within the learning process and distract...
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