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United we stand, Divided we fall: Handling Weak Complementary Relationships for Audio-Visual Emotion Recognition in Valence-Arousal Space

R. Gnana Praveen, Jahangir Alam, Eric Charton

TL;DR

The paper addresses the challenge of weak complementary relationships between audio and visual cues in dimensional emotion recognition using valence and arousal. It introduces GRJCA, which employs a gating mechanism to selectively emphasize cross-attended versus unattended features across iterative RJCA steps, and extends this with a hierarchical gating variant (HGRJCA) for finer-grained fusion. Through extensive experiments on Affwild2, GRJCA and HGRJCA demonstrate improved robustness and competitive CCC scores, achieving valence arousal CCCs around 0.56–0.62 on standard splits, and up to 0.56/0.62 on the test set. The work highlights gating as a practical strategy to handle varying inter-modal complementarities in the wild, enabling more reliable audio-visual emotion recognition without excessive data or ensembling.

Abstract

Audio and visual modalities are two predominant contact-free channels in videos, which are often expected to carry a complementary relationship with each other. However, they may not always complement each other, resulting in poor audio-visual feature representations. In this paper, we introduce Gated Recursive Joint Cross Attention (GRJCA) using a gating mechanism that can adaptively choose the most relevant features to effectively capture the synergic relationships across audio and visual modalities. Specifically, we improve the performance of Recursive Joint Cross-Attention (RJCA) by introducing a gating mechanism to control the flow of information between the input features and the attended features of multiple iterations depending on the strength of their complementary relationship. For instance, if the modalities exhibit strong complementary relationships, the gating mechanism emphasizes cross-attended features, otherwise non-attended features. To further improve the performance of the system, we also explored a hierarchical gating approach by introducing a gating mechanism at every iteration, followed by high-level gating across the gated outputs of each iteration. The proposed approach improves the performance of RJCA model by adding more flexibility to deal with weak complementary relationships across audio and visual modalities. Extensive experiments are conducted on the challenging Affwild2 dataset to demonstrate the robustness of the proposed approach. By effectively handling the weak complementary relationships across the audio and visual modalities, the proposed model achieves a Concordance Correlation Coefficient (CCC) of 0.561 (0.623) and 0.620 (0.660) for valence and arousal respectively on the test set (validation set).

United we stand, Divided we fall: Handling Weak Complementary Relationships for Audio-Visual Emotion Recognition in Valence-Arousal Space

TL;DR

The paper addresses the challenge of weak complementary relationships between audio and visual cues in dimensional emotion recognition using valence and arousal. It introduces GRJCA, which employs a gating mechanism to selectively emphasize cross-attended versus unattended features across iterative RJCA steps, and extends this with a hierarchical gating variant (HGRJCA) for finer-grained fusion. Through extensive experiments on Affwild2, GRJCA and HGRJCA demonstrate improved robustness and competitive CCC scores, achieving valence arousal CCCs around 0.56–0.62 on standard splits, and up to 0.56/0.62 on the test set. The work highlights gating as a practical strategy to handle varying inter-modal complementarities in the wild, enabling more reliable audio-visual emotion recognition without excessive data or ensembling.

Abstract

Audio and visual modalities are two predominant contact-free channels in videos, which are often expected to carry a complementary relationship with each other. However, they may not always complement each other, resulting in poor audio-visual feature representations. In this paper, we introduce Gated Recursive Joint Cross Attention (GRJCA) using a gating mechanism that can adaptively choose the most relevant features to effectively capture the synergic relationships across audio and visual modalities. Specifically, we improve the performance of Recursive Joint Cross-Attention (RJCA) by introducing a gating mechanism to control the flow of information between the input features and the attended features of multiple iterations depending on the strength of their complementary relationship. For instance, if the modalities exhibit strong complementary relationships, the gating mechanism emphasizes cross-attended features, otherwise non-attended features. To further improve the performance of the system, we also explored a hierarchical gating approach by introducing a gating mechanism at every iteration, followed by high-level gating across the gated outputs of each iteration. The proposed approach improves the performance of RJCA model by adding more flexibility to deal with weak complementary relationships across audio and visual modalities. Extensive experiments are conducted on the challenging Affwild2 dataset to demonstrate the robustness of the proposed approach. By effectively handling the weak complementary relationships across the audio and visual modalities, the proposed model achieves a Concordance Correlation Coefficient (CCC) of 0.561 (0.623) and 0.620 (0.660) for valence and arousal respectively on the test set (validation set).

Paper Structure

This paper contains 21 sections, 13 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. A-V fusion for dimensional ER
  4. Attention for A-V fusion
  5. Gating-Based Attention for A-V Fusion
  6. Proposed Approach
  7. Notations
  8. Recursive Joint Cross Attention (RJCA)
  9. Gated Recursive Joint Cross Attention (GRJCA)
  10. Hierarchical Gated Recursive Joint Cross Attention (HGRJCA)
  11. Experimental Setup
  12. Datasets
  13. Evaluation Metric
  14. Implementation Details
  15. Preprocessing
  16. ...and 6 more sections

Figures (2)

  • Figure 1: Illustration of the proposed GRJCA model for two iterations. Different colorized arrows are used to denote the gating mechanism. Best viewed in color.
  • Figure 2: Illustration of the proposed HGRJCA model with two iterations. Different colorized arrows are used to denote different gating layers. Best viewed in color.