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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking

February 1, 2023

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Authors

Yidi Li

Peking University, Shenzhen Graduate School


Hong Liu

Peking University, Shenzhen Graduate School


Hao Tang

ETH Zurich


DOI:

10.1609/aaai.v36i2.20035


Abstract:

Multi-modal fusion is proven to be an effective method to improve the accuracy and robustness of speaker tracking, especially in complex scenarios. However, how to combine the heterogeneous information and exploit the complementarity of multi-modal signals remains a challenging issue. In this paper, we propose a novel Multi-modal Perception Tracker (MPT) for speaker tracking using both audio and visual modalities. Specifically, a novel acoustic map based on spatial-temporal Global Coherence Field (stGCF) is first constructed for heterogeneous signal fusion, which employs a camera model to map audio cues to the localization space consistent with the visual cues. Then a multi-modal perception attention network is introduced to derive the perception weights that measure the reliability and effectiveness of intermittent audio and video streams disturbed by noise. Moreover, a unique cross-modal self-supervised learning method is presented to model the confidence of audio and visual observations by leveraging the complementarity and consistency between different modalities. Experimental results show that the proposed MPT achieves 98.6% and 78.3% tracking accuracy on the standard and occluded datasets, respectively, which demonstrates its robustness under adverse conditions and outperforms the current state-of-the-art methods.

Topics: AAAI

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HOW TO CITE:

Yidi Li||Hong Liu||Hao Tang Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1456-1463.

Yidi Li||Hong Liu||Hao Tang Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking AAAI 2022, 1456-1463.

Yidi Li||Hong Liu||Hao Tang (2022). Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking. Proceedings of the AAAI Conference on Artificial Intelligence, 1456-1463.

Yidi Li||Hong Liu||Hao Tang. Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1456-1463.

Yidi Li||Hong Liu||Hao Tang. 2022. Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking. "Proceedings of the AAAI Conference on Artificial Intelligence". 1456-1463.

Yidi Li||Hong Liu||Hao Tang. (2022) "Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking", Proceedings of the AAAI Conference on Artificial Intelligence, p.1456-1463

Yidi Li||Hong Liu||Hao Tang, "Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking", AAAI, p.1456-1463, 2022.

Yidi Li||Hong Liu||Hao Tang. "Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1456-1463.

Yidi Li||Hong Liu||Hao Tang. "Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1456-1463.

Yidi Li||Hong Liu||Hao Tang. Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking. AAAI[Internet]. 2022[cited 2023]; 1456-1463.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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