  Recent work has proven the effectiveness of transformers in many computer
vision tasks. However, the performance of transformers in gaze estimation is
still unexplored. In this paper, we employ transformers and assess their
effectiveness for gaze estimation. We consider two forms of vision transformer
which are pure transformers and hybrid transformers. We first follow the
popular ViT and employ a pure transformer to estimate gaze from images. On the
other hand, we preserve the convolutional layers and integrate CNNs as well as
transformers. The transformer serves as a component to complement CNNs. We
compare the performance of the two transformers in gaze estimation. The Hybrid
transformer significantly outperforms the pure transformer in all evaluation
datasets with less parameters. We further conduct experiments to assess the
effectiveness of the hybrid transformer and explore the advantage of
self-attention mechanism. Experiments show the hybrid transformer can achieve
state-of-the-art performance in all benchmarks with pre-training.To facilitate
further research, we release codes and models in
https://github.com/yihuacheng/GazeTR.
