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Over the past few years, there has been an increasing interest to interpretgaze direction in an unconstrained environment with limited supervision. Owingto data curation and annotation issues, replicating gaze estimation method toother platforms, such as unconstrained outdoor or AR/VR, might lead tosignificant drop in performance due to insufficient availability of accuratelyannotated data for model training. In this paper, we explore an interesting yetchallenging problem of gaze estimation method with a limited amount of labelleddata. The proposed method distills knowledge from the labelled subset withvisual features; including identity-specific appearance, gaze trajectoryconsistency and motion features. Given a gaze trajectory, the method utilizeslabel information of only the start and the end frames of a gaze sequence. Anextension of the proposed method further reduces the requirement of labelledframes to only the start frame with a minor drop in the generated label'squality. We evaluate the proposed method on four benchmark datasets (CAVE,TabletGaze, MPII and Gaze360) as well as web-crawled YouTube videos. Ourproposed method reduces the annotation effort to as low as 2.67%, with minimalimpact on performance; indicating the potential of our model enabling gazeestimation 'in-the-wild' setup.

'Labelling the Gaps': A Weakly Supervised Automatic Eye Gaze Estimation