A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition

• URL: http://arxiv.org/abs/2203.14779v4
• Date: Sat, 6 Jul 2024 14:49:19 GMT
• Title: A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition
• Authors: R. Gnana Praveen, Wheidima Carneiro de Melo, Nasib Ullah, Haseeb Aslam, Osama Zeeshan, Théo Denorme, Marco Pedersoli, Alessandro Koerich, Simon Bacon, Patrick Cardinal, Eric Granger,
• Abstract summary: We focus on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos. We propose a joint cross-attention model that relies on the complementary relationships to extract the salient features. Our proposed A-V fusion model provides a cost-effective solution that can outperform state-of-the-art approaches.
• Score: 46.443866373546726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal emotion recognition has recently gained much attention since it can leverage diverse and complementary relationships over multiple modalities (e.g., audio, visual, biosignals, etc.), and can provide some robustness to noisy modalities. Most state-of-the-art methods for audio-visual (A-V) fusion rely on recurrent networks or conventional attention mechanisms that do not effectively leverage the complementary nature of A-V modalities. In this paper, we focus on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos. Specifically, we propose a joint cross-attention model that relies on the complementary relationships to extract the salient features across A-V modalities, allowing for accurate prediction of continuous values of valence and arousal. The proposed fusion model efficiently leverages the inter-modal relationships, while reducing the heterogeneity between the features. In particular, it computes the cross-attention weights based on correlation between the combined feature representation and individual modalities. By deploying the combined A-V feature representation into the cross-attention module, the performance of our fusion module improves significantly over the vanilla cross-attention module. Experimental results on validation-set videos from the AffWild2 dataset indicate that our proposed A-V fusion model provides a cost-effective solution that can outperform state-of-the-art approaches. The code is available on GitHub: https://github.com/praveena2j/JointCrossAttentional-AV-Fusion.

Related papers

• Dynamic Cross Attention for Audio-Visual Person Verification [3.5803801804085347]
  We propose a Dynamic Cross-Attention (DCA) model that can dynamically select the cross-attended or unattended features on the fly. In particular, a conditional gating layer is designed to evaluate the contribution of the cross-attention mechanism. Extensive experiments are conducted on the Voxceleb1 dataset to demonstrate the robustness of the proposed model.
  arXiv  Detail & Related papers  (2024-03-07T17:07:51Z)
• Audio-Visual Person Verification based on Recursive Fusion of Joint Cross-Attention [3.5803801804085347]
  We introduce a joint cross-attentional model, where a joint audio-visual feature representation is employed in the cross-attention framework. We also explore BLSTMs to improve the temporal modeling of audio-visual feature representations. Results indicate that the proposed model shows promising improvement in fusion performance by adeptly capturing the intra-and inter-modal relationships.
  arXiv  Detail & Related papers  (2024-03-07T16:57:45Z)
• Recursive Joint Attention for Audio-Visual Fusion in Regression based Emotion Recognition [15.643176705932396]
  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.
  arXiv  Detail & Related papers  (2023-04-17T02:57:39Z)
• Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention [15.643176705932396]
  We introduce a joint cross-attentional model for A-V fusion that extracts the salient features across A-V modalities. It computes the cross-attention weights based on correlation between the joint feature representation and that of the individual modalities. Results indicate that our joint cross-attentional A-V fusion model provides a cost-effective solution that can outperform state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-09-19T15:01:55Z)
• MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis [84.7287684402508]
  Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations. Models of human perception highlight the importance of top-down fusion, where high-level representations affect the way sensory inputs are perceived. We propose a neural architecture that captures top-down cross-modal interactions, using a feedback mechanism in the forward pass during network training.
  arXiv  Detail & Related papers  (2022-01-24T17:48:04Z)
• Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition [13.994609732846344]
  Most effective techniques for emotion recognition efficiently leverage diverse and complimentary sources of information. We introduce a cross-attentional fusion approach to extract the salient features across audio-visual (A-V) modalities. Results indicate that our cross-attentional A-V fusion model is a cost-effective approach that outperforms state-of-the-art fusion approaches.
  arXiv  Detail & Related papers  (2021-11-09T16:01:56Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)
• Attention Bottlenecks for Multimodal Fusion [90.75885715478054]
  Machine perception models are typically modality-specific and optimised for unimodal benchmarks. We introduce a novel transformer based architecture that uses fusion' for modality fusion at multiple layers. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks.
  arXiv  Detail & Related papers  (2021-06-30T22:44:12Z)
• Learning Multimodal VAEs through Mutual Supervision [72.77685889312889]
  MEME combines information between modalities implicitly through mutual supervision. We demonstrate that MEME outperforms baselines on standard metrics across both partial and complete observation schemes.
  arXiv  Detail & Related papers  (2021-06-23T17:54:35Z)
• Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification [208.1227090864602]
  Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Existing VI-ReID methods tend to learn global representations, which have limited discriminability and weak robustness to noisy images. We propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.
  arXiv  Detail & Related papers  (2020-07-18T03:08:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.