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Predicting human gaze is important in Human-Computer Interaction (HCI).However, to practically serve HCI applications, gaze prediction models must bescalable, fast, and accurate in their spatial and temporal gaze predictions.Recent scanpath prediction models focus on goal-directed attention (search).Such models are limited in their application due to a common approach relyingon trained target detectors for all possible objects, and the availability ofhuman gaze data for their training (both not scalable). In response, we pose anew task called ZeroGaze, a new variant of zero-shot learning where gaze ispredicted for never-before-searched objects, and we develop a novel model,Gazeformer, to solve the ZeroGaze problem. In contrast to existing methodsusing object detector modules, Gazeformer encodes the target using a naturallanguage model, thus leveraging semantic similarities in scanpath prediction.We use a transformer-based encoder-decoder architecture because transformersare particularly useful for generating contextual representations. Gazeformersurpasses other models by a large margin on the ZeroGaze setting. It alsooutperforms existing target-detection models on standard gaze prediction forboth target-present and target-absent search tasks. In addition to its improvedperformance, Gazeformer is more than five times faster than thestate-of-the-art target-present visual search model.

Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention