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

arXiv: https://arxiv.org/abs/2203.14779 · HTML: https://mlsanity.com/paper/2203.14779/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 58 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Attention Bottlenecks for Multimodal Fusion](https://mlsanity.com/paper/2107.00135/) (2021)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Cross Attention Network for Few-shot Classification](https://mlsanity.com/paper/1910.07677/) (2019)
- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [Analysing Affective Behavior in the First ABAW 2020 Competition](https://mlsanity.com/paper/2001.11409/) (2020)
- [Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework](https://mlsanity.com/paper/2103.15792/) (2021)
- [Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study](https://mlsanity.com/paper/2105.03790/) (2021)
- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [Two-Stream Aural-Visual Affect Analysis in the Wild](https://mlsanity.com/paper/2002.03399/) (2020)
- [Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention](https://mlsanity.com/paper/2008.06581/) (2020)
- [Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2103.09154/) (2021)
- [Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition](https://mlsanity.com/paper/2108.04228/) (2021)
- [Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3](https://mlsanity.com/paper/2203.13031/) (2022)
- [A Multi-task Mean Teacher for Semi-supervised Facial Affective Behavior Analysis](https://mlsanity.com/paper/2107.04225/) (2021)
- [Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition](https://mlsanity.com/paper/2111.05222/) (2021)
- [Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices](https://mlsanity.com/paper/2203.13436/) (2022)
- [Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion](https://mlsanity.com/paper/2107.01175/) (2021)
- [Multi-modal Emotion Estimation for in-the-wild Videos](https://mlsanity.com/paper/2203.13032/) (2022)
- [M3T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild](https://mlsanity.com/paper/2002.02957/) (2020)
- [Detecting expressions with multimodal transformers](https://mlsanity.com/paper/2012.00063/) (2020)
- [Deep Weakly-Supervised Domain Adaptation for Pain Localization in Videos](https://mlsanity.com/paper/1910.08173/) (2019)
- [Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition](https://mlsanity.com/paper/2004.13236/) (2020)
- [Emotion Recognition Using Fusion of Audio and Video Features](https://mlsanity.com/paper/1906.10623/) (2019)

## Built on by (most cited first, 30 of 34)

- [ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.01138/) (2022)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach](https://mlsanity.com/paper/2406.10581/) (2024)
- [Listen to Look into the Future: Audio-Visual Egocentric Gaze Anticipation](https://mlsanity.com/paper/2305.03907/) (2023)
- [CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2307.05182/) (2023)
- [Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic Surgery](https://mlsanity.com/paper/2408.04958/) (2024)
- [Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport](https://mlsanity.com/paper/2401.15489/) (2024)
- [Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition](https://mlsanity.com/paper/2403.13659/) (2024)
- [Joint Multimodal Transformer for Emotion Recognition in the Wild](https://mlsanity.com/paper/2403.10488/) (2024)
- [Conflict-Aware Multimodal Fusion for Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2603.15818/) (2026)
- [MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images](https://mlsanity.com/paper/2412.09402/) (2024)
- [MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network](https://mlsanity.com/paper/2503.12623/) (2025)
- [Recursive Joint Attention for Audio-Visual Fusion in Regression based Emotion Recognition](https://mlsanity.com/paper/2304.07958/) (2023)
- [Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue](https://mlsanity.com/paper/2402.03658/) (2024)
- [Affective Video Content Analysis: Decade Review and New Perspectives](https://mlsanity.com/paper/2310.17212/) (2023)
- [Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation](https://mlsanity.com/paper/2406.06163/) (2024)
- [Audio-Visual Speaker Verification via Joint Cross-Attention](https://mlsanity.com/paper/2309.16569/) (2023)
- [MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training](https://mlsanity.com/paper/2508.12522/) (2025)
- [Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition](https://mlsanity.com/paper/2408.09035/) (2024)
- [Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification](https://mlsanity.com/paper/2408.14441/) (2024)
- [Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting](https://mlsanity.com/paper/2508.02429/) (2025)
- [Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions](https://mlsanity.com/paper/2604.11730/) (2026)
- [FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference](https://mlsanity.com/paper/2209.10170/) (2022)
- [Inconsistency-Aware Cross-Attention for Audio-Visual Fusion in Dimensional Emotion Recognition](https://mlsanity.com/paper/2405.12853/) (2024)
- [Stage-Adaptive Reliability Modeling for Continuous Valence-Arousal Estimation](https://mlsanity.com/paper/2603.11468/) (2026)
- [Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction](https://mlsanity.com/paper/2607.22729/) (2026)
- [Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities](https://mlsanity.com/paper/2404.12251/) (2024)
- [Music Genre Classification using Large Language Models](https://mlsanity.com/paper/2410.08321/) (2024)

## Abstract (arXiv)

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.