  Egocentric gaze anticipation serves as a key building block for the emerging
capability of Augmented Reality. Notably, gaze behavior is driven by both
visual cues and audio signals during daily activities. Motivated by this
observation, we introduce the first model that leverages both the video and
audio modalities for egocentric gaze anticipation. Specifically, we propose a
Contrastive Spatial-Temporal Separable (CSTS) fusion approach that adopts two
modules to separately capture audio-visual correlations in spatial and temporal
dimensions, and applies a contrastive loss on the re-weighted audio-visual
features from fusion modules for representation learning. We conduct extensive
ablation studies and thorough analysis using two egocentric video datasets:
Ego4D and Aria, to validate our model design. We demonstrate the audio improves
the performance by +2.5% and +2.4% on the two datasets. Our model also
outperforms the prior state-of-the-art methods by at least +1.9% and +1.6%.
Moreover, we provide visualizations to show the gaze anticipation results and
provide additional insights into audio-visual representation learning. The code
and data split are available on our website
(https://bolinlai.github.io/CSTS-EgoGazeAnticipation/).
