Eye gaze has proven to be a cost-efficient way to collect large-scale physiological data that can reveal the underlying human attentional patterns in real-life workflows, and thus has long been explored as a signal to directly measure human-related cognition in various domains. Physiological data (including but not limited to eye gaze) offer new perception capabilities, which could be used in several ML domains, e.g., egocentric perception, embodied AI, NLP, etc. They can help infer human perception, intentions, beliefs, goals, and other cognition properties that are much needed for human-AI interactions and agent coordination. In addition, large collections of eye-tracking data have enabled data-driven modeling of human visual attention mechanisms, both for saliency or scan path prediction, with twofold advantages: from the neuro-scientific perspective to understand biological mechanisms better, and from the AI perspective to equip agents with the ability to mimic or predict human behavior and improve interpretability and interactions.

The Gaze meets ML workshop aims at bringing together an active research community to collectively drive progress in defining and addressing core problems in gaze-assisted machine learning. This year the workshop will run its 2nd edition at NeurIPS again and it attracts a diverse group of researchers from academia and industry presenting novel works in this area of research.