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United we stand, Divided we fall: Handling Weak Complementary Relationships for Audio-Visual Emotion Recognition in Valence-Arousal Space
Computer Vision and Pattern Recognition · Submitted 15 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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).
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The abstract, sentence by sentence
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).
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Audio and visual modalities are two predominant contact-free channels in videos, which are often expected to carry a complementary relationship with each other. | background 1.00 | 0.04 | 0.02 | asserted 0.51 |
| s1 | However, they may not always complement each other, resulting in poor audio-visual feature representations. | background 0.53 | 0.10 | 0.04 | hedged possibility 1.00 |
| s2 | 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. | contribution 0.79 | 0.93 | 0.16 | asserted 1.00 |
| s3 | 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. | method 0.94 | 0.92 | 0.09 | asserted 1.00 |
| s4 | For instance, if the modalities exhibit strong complementary relationships, the gating mechanism emphasizes cross-attended features, otherwise non-attended features. | method 0.99 | 0.50 | 0.08 | asserted 0.95 |
| s5 | 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. | method 0.89 | 0.93 | 0.05 | asserted 0.74 |
| s6 | The proposed approach improves the performance of RJCA model by adding more flexibility to deal with weak complementary relationships across audio and visual modalities. | contribution 0.51 | 0.80 | 0.40 | asserted 1.00 |
| s7 | Extensive experiments are conducted on the challenging Affwild2 dataset to demonstrate the robustness of the proposed approach. | method 0.71 | 0.94 | 0.06 | asserted 1.00 |
| s8 | 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). | result 1.00 | 0.97 | 0.54 | asserted 1.00 |
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The paper
Original 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).
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