Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach

• URL: http://arxiv.org/abs/2507.02205v2
• Date: Fri, 04 Jul 2025 14:42:15 GMT
• Title: Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach
• Authors: Elena Ryumina, Maxim Markitantov, Alexandr Axyonov, Dmitry Ryumin, Mikhail Dolgushin, Alexey Karpov,
• Abstract summary: Compound Expression Recognition (CER) aims to detect complex emotional states formed by combinations of basic emotions.<n>We present a novel zero-shot multimodal approach for CER that combines six heterogeneous modalities into a single pipeline.<n>The proposed approach shows F1 scores of 46.95% on AffWild2, 49.02% on Acted Facial Expressions in The Wild (AFEW), and 34.85% on C-EXPR-DB via zero-shot testing.
• Score: 44.40745123728199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compound Expression Recognition (CER), a subfield of affective computing, aims to detect complex emotional states formed by combinations of basic emotions. In this work, we present a novel zero-shot multimodal approach for CER that combines six heterogeneous modalities into a single pipeline: static and dynamic facial expressions, scene and label matching, scene context, audio, and text. Unlike previous approaches relying on task-specific training data, our approach uses zero-shot components, including Contrastive Language-Image Pretraining (CLIP)-based label matching and Qwen-VL for semantic scene understanding. We further introduce a Multi-Head Probability Fusion (MHPF) module that dynamically weights modality-specific predictions, followed by a Compound Expressions (CE) transformation module that uses Pair-Wise Probability Aggregation (PPA) and Pair-Wise Feature Similarity Aggregation (PFSA) methods to produce interpretable compound emotion outputs. Evaluated under multi-corpus training, the proposed approach shows F1 scores of 46.95% on AffWild2, 49.02% on Acted Facial Expressions in The Wild (AFEW), and 34.85% on C-EXPR-DB via zero-shot testing, which is comparable to the results of supervised approaches trained on target data. This demonstrates the effectiveness of the proposed approach for capturing CE without domain adaptation. The source code is publicly available.

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