Recursive Joint Attention for Audio-Visual Fusion in Regression based Emotion Recognition

• URL: http://arxiv.org/abs/2304.07958v1
• Date: Mon, 17 Apr 2023 02:57:39 GMT
• Title: Recursive Joint Attention for Audio-Visual Fusion in Regression based Emotion Recognition
• Authors: R Gnana Praveen, Eric Granger, Patrick Cardinal
• Abstract summary: In video-based emotion recognition, it is important to leverage the complementary relationship among audio (A) and visual (V) modalities. In this paper, we investigate the possibility of exploiting the complementary nature of A and V modalities using a joint cross-attention model. Our model can efficiently leverage both intra- and inter-modal relationships for the fusion of A and V modalities.
• Score: 15.643176705932396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In video-based emotion recognition (ER), it is important to effectively leverage the complementary relationship among audio (A) and visual (V) modalities, while retaining the intra-modal characteristics of individual modalities. In this paper, a recursive joint attention model is proposed along with long short-term memory (LSTM) modules for the fusion of vocal and facial expressions in regression-based ER. Specifically, we investigated the possibility of exploiting the complementary nature of A and V modalities using a joint cross-attention model in a recursive fashion with LSTMs to capture the intra-modal temporal dependencies within the same modalities as well as among the A-V feature representations. By integrating LSTMs with recursive joint cross-attention, our model can efficiently leverage both intra- and inter-modal relationships for the fusion of A and V modalities. The results of extensive experiments performed on the challenging Affwild2 and Fatigue (private) datasets indicate that the proposed A-V fusion model can significantly outperform state-of-art-methods.

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