  In challenging real-life conditions such as extreme head-pose, occlusions,
and low-resolution images where the visual information fails to estimate visual
attention/gaze direction, audio signals could provide important and
complementary information. In this paper, we explore if audio-guided coarse
head-pose can further enhance visual attention estimation performance for
non-prolific faces. Since it is difficult to annotate audio signals for
estimating the head-pose of the speaker, we use off-the-shelf state-of-the-art
models to facilitate cross-modal weak-supervision. During the training phase,
the framework learns complementary information from synchronized audio-visual
modality. Our model can utilize any of the available modalities i.e. audio,
visual or audio-visual for task-specific inference. It is interesting to note
that, when AV-Gaze is tested on benchmark datasets with these specific
modalities, it achieves competitive results on multiple datasets, while being
highly adaptive toward challenging scenarios.
