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<p>This study explores the integration of information-rich prior knowledge, specifically human gaze data, to enhance representation learning through contrastive methods. We propose gaze-guided contrastive unsupervised representation learning, a novel framework harnessing human gaze data to guide the selection of positive and negative samples for contrastive learning. By leveraging human gaze information, we capture meaningful patterns in visual task dynamics, enabling the agent to acquire effective strategies from demonstrations and achieve superior performance. Our findings demonstrate significant improvements over baseline algorithms, highlighting the value of gaze-guided representation learning in reducing data requirements and accelerating learning. This approach offers broad applicability to vision-based tasks, emphasizing the critical role of human gaze in improving task efficiency and generalization.</p>

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Gaze-guided contrastive unsupervised representations learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

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  • Joseph P. Distefano,
  • Hemanth Manjunatha,
  • Chaithanya Thammineni,
  • Kristian Dalland,
  • Ehsan T. Esfahani

摘要

This study explores the integration of information-rich prior knowledge, specifically human gaze data, to enhance representation learning through contrastive methods. We propose gaze-guided contrastive unsupervised representation learning, a novel framework harnessing human gaze data to guide the selection of positive and negative samples for contrastive learning. By leveraging human gaze information, we capture meaningful patterns in visual task dynamics, enabling the agent to acquire effective strategies from demonstrations and achieve superior performance. Our findings demonstrate significant improvements over baseline algorithms, highlighting the value of gaze-guided representation learning in reducing data requirements and accelerating learning. This approach offers broad applicability to vision-based tasks, emphasizing the critical role of human gaze in improving task efficiency and generalization.