Institution profile

Université Vincennes Saint-Denis

Academic institutioneurope · fr
Official website
Research library12linked papers
Opportunities0open roles
Selected work

Representative Papers

Sparse Attention to Emotion: Efficient Facial Emotion Recognition via Token Reduction

Aug 09, 2026

This work addresses the challenge of deploying vision Transformer-based facial emotion recognition models on edge devices, where their O(N²) computational complexity poses significant limitations. To this end, we propose Sparse Attention to Emotion (SAE), the first approach to incorporate sparse attention mechanisms into this task. SAE dynamically prunes image tokens irrelevant to emotion discrimination, retaining only those from critical regions such as the eyes and mouth. Experimental results demonstrate that SAE achieves a new state-of-the-art accuracy on the RAF-DB dataset using approximately 10% of the original tokens, while reducing computational complexity by up to 90%. This substantial efficiency gain markedly enhances the model’s practicality for real-world deployment without compromising performance.

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Multimodal Ambivalence and Hesitancy Recognition via Cross-Attention and Gated Fusion

Jul 17, 2026

This work addresses the challenging task of recognizing individual contradiction and hesitation states in videos—a key problem in affective computing and human-computer interaction—by proposing a novel multimodal approach that integrates textual, audio, and visual modalities. The method leverages F2LLM-v2-0.6B, WavLM-Large, and VideoMAE V2 to extract modality-specific features and introduces a fusion architecture combining bidirectional cross-attention with gated multimodal units to effectively model complementary cross-modal information. Evaluated on the ABAW11 challenge, the proposed approach achieves a Macro F1 score of 0.7394 on the validation set, representing an 11.0% improvement over the best single-modality baseline and significantly outperforming both unimodal and zero-shot methods.

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Recent publications

Latest Papers

Sparse Attention to Emotion: Efficient Facial Emotion Recognition via Token Reduction

Aug 09, 2026

This work addresses the challenge of deploying vision Transformer-based facial emotion recognition models on edge devices, where their O(N²) computational complexity poses significant limitations. To this end, we propose Sparse Attention to Emotion (SAE), the first approach to incorporate sparse attention mechanisms into this task. SAE dynamically prunes image tokens irrelevant to emotion discrimination, retaining only those from critical regions such as the eyes and mouth. Experimental results demonstrate that SAE achieves a new state-of-the-art accuracy on the RAF-DB dataset using approximately 10% of the original tokens, while reducing computational complexity by up to 90%. This substantial efficiency gain markedly enhances the model’s practicality for real-world deployment without compromising performance.

0 citationsRead paper

Multimodal Ambivalence and Hesitancy Recognition via Cross-Attention and Gated Fusion

Jul 17, 2026

This work addresses the challenging task of recognizing individual contradiction and hesitation states in videos—a key problem in affective computing and human-computer interaction—by proposing a novel multimodal approach that integrates textual, audio, and visual modalities. The method leverages F2LLM-v2-0.6B, WavLM-Large, and VideoMAE V2 to extract modality-specific features and introduces a fusion architecture combining bidirectional cross-attention with gated multimodal units to effectively model complementary cross-modal information. Evaluated on the ABAW11 challenge, the proposed approach achieves a Macro F1 score of 0.7394 on the validation set, representing an 11.0% improvement over the best single-modality baseline and significantly outperforming both unimodal and zero-shot methods.

0 citationsRead paper