Dynamic Inter-Class Confusion-Aware Encoder for Audio-Visual Fusion in Human Activity Recognition

• URL: http://arxiv.org/abs/2507.09323v1
• Date: Sat, 12 Jul 2025 15:45:36 GMT
• Title: Dynamic Inter-Class Confusion-Aware Encoder for Audio-Visual Fusion in Human Activity Recognition
• Authors: Kaixuan Cong, Yifan Wang, Rongkun Xue, Yuyang Jiang, Yiming Feng, Jing Yang,
• Abstract summary: This paper proposes the Dynamic Inter-Class Confusion-Aware (DICCAE), an encoder that aligns audio-video representations at a fine-grained, category-level.<n>DICCAE addresses category confusion by dynamically adjusting the confusion loss based on inter-class confusion degrees.<n>We also introduce a novel training framework that incorporates both audio and video modalities, as well as their fusion.
• Score: 6.814894552541548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans do not understand individual events in isolation; rather, they generalize concepts within classes and compare them to others. Existing audio-video pre-training paradigms only focus on the alignment of the overall audio-video modalities, without considering the reinforcement of distinguishing easily confused classes through cognitive induction and contrast during training. This paper proposes the Dynamic Inter-Class Confusion-Aware Encoder (DICCAE), an encoder that aligns audio-video representations at a fine-grained, category-level. DICCAE addresses category confusion by dynamically adjusting the confusion loss based on inter-class confusion degrees, thereby enhancing the model's ability to distinguish between similar activities. To further extend the application of DICCAE, we also introduce a novel training framework that incorporates both audio and video modalities, as well as their fusion. To mitigate the scarcity of audio-video data in the human activity recognition task, we propose a cluster-guided audio-video self-supervised pre-training strategy for DICCAE. DICCAE achieves near state-of-the-art performance on the VGGSound dataset, with a top-1 accuracy of 65.5%. We further evaluate its feature representation quality through extensive ablation studies, validating the necessity of each module.

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