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• W4308238077 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 toward challenging scenarios." @default.
• W4308238077 created "2022-11-09" @default.
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• W4308238077 date "2022-10-16" @default.
• W4308238077 modified "2023-10-14" @default.
• W4308238077 title "AV-GAZE: A Study on the Effectiveness of Audio Guided Visual Attention Estimation for Non-profilic Faces" @default.
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• W4308238077 doi "https://doi.org/10.1109/icip46576.2022.9897360" @default.
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