Gaze-guided contrastive unsupervised representations learning

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.