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

AV-Gaze: A Study on the Effectiveness of Audio Guided Visual Attention Estimation for Non-Profilic Faces