  Recent joint embedding-based self-supervised methods have surpassed standard
supervised approaches on various image recognition tasks such as image
classification. These self-supervised methods aim at maximizing agreement
between features extracted from two differently transformed views of the same
image, which results in learning an invariant representation with respect to
appearance and geometric image transformations. However, the effectiveness of
these approaches remains unclear in the context of gaze estimation, a
structured regression task that requires equivariance under geometric
transformations (e.g., rotations, horizontal flip). In this work, we propose
SwAT, an equivariant version of the online clustering-based self-supervised
approach SwAV, to learn more informative representations for gaze estimation.
We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled
face images, outperforms state-of-the-art gaze estimation methods and
supervised baselines in various experiments. In particular, we achieve up to
57% and 25% improvements in cross-dataset and within-dataset evaluation tasks
on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).
