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

• URL: http://arxiv.org/abs/2207.03048v1
• Date: Thu, 7 Jul 2022 02:23:02 GMT
• Title: AV-Gaze: A Study on the Effectiveness of Audio Guided Visual Attention Estimation for Non-Profilic Faces
• Authors: Shreya Ghosh, Abhinav Dhall, Munawar Hayat, Jarrod Knibbe
• Abstract summary: In this paper, we explore if audio-guided coarse head-pose can further enhance visual attention estimation performance for non-prolific faces. We use off-the-shelf state-of-the-art models to facilitate cross-modal weak-supervision. Our model can utilize any of the available modalities for task-specific inference.
• Score: 28.245662058349854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 towards challenging scenarios.

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