  Gaze following aims to predict where a person is looking in a scene, by
predicting the target location, or indicating that the target is located
outside the image. Recent works detect the gaze target by training a heatmap
regression task with a pixel-wise mean-square error (MSE) loss, while
formulating the in/out prediction task as a binary classification task. This
training formulation puts a strict, pixel-level constraint in higher resolution
on the single annotation available in training, and does not consider
annotation variance and the correlation between the two subtasks. To address
these issues, we introduce the patch distribution prediction (PDP) method. We
replace the in/out prediction branch in previous models with the PDP branch, by
predicting a patch-level gaze distribution that also considers the outside
cases. Experiments show that our model regularizes the MSE loss by predicting
better heatmap distributions on images with larger annotation variances,
meanwhile bridging the gap between the target prediction and in/out prediction
subtasks, showing a significant improvement in performance on both subtasks on
public gaze following datasets.
