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In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation
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In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scene context and local visual information is vital for localizing the gaze fixation from egocentric video frames. To this end, we design the transformer encoder to embed the global context as one additional visual token and further propose a novel Global-Local Correlation (GLC) module to explicitly model the correlation of the global token and each local token. We validate our model on two egocentric video datasets - EGTEA Gaze+ and Ego4D. Our detailed ablation studies demonstrate the benefits of our method. In addition, our approach exceeds previous state-of-the-arts by a large margin. We also provide additional visualizations to support our claim that global-local correlation serves a key representation for predicting gaze fixation from egocentric videos. More details can be found in our website (https://bolinlai.github.io/GLC-EgoGazeEst).
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Cited by 1 Pith paper
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EgoAdapt: Adaptive Multisensory Distillation and Policy Learning for Efficient Egocentric Perception
A joint distillation and policy-learning framework claims near-teacher accuracy on egocentric action recognition, active speaker localization, and behavior anticipation at a fraction of the compute.
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