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Volume 47 Issue 7
Jul.  2025
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YANG Jing, LI Xiaoyong, RUAN Xiaoli, LI Shaobo, TANG Xianghong, XU Ji. An Audio-visual Generalized Zero-Shot Learning Method Based on Multimodal Fusion Transformer[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2375-2384. doi: 10.11999/JEIT241090
Citation: YANG Jing, LI Xiaoyong, RUAN Xiaoli, LI Shaobo, TANG Xianghong, XU Ji. An Audio-visual Generalized Zero-Shot Learning Method Based on Multimodal Fusion Transformer[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2375-2384. doi: 10.11999/JEIT241090

An Audio-visual Generalized Zero-Shot Learning Method Based on Multimodal Fusion Transformer

doi: 10.11999/JEIT241090 cstr: 32379.14.JEIT241090v
Funds:  The National Natural Science Foundation of China (62441608,62166005), The Science and Technology Project of Guizhou Province (QKHZC[2023]368), The Developing Objects and Projects of Scientific and Technological Talents in Guiyang City (ZKHT[2023]48-8), Guizhou University Basic Research Fund ([2024]08), The Open Project of State Key Laboratory of Public Big Data (PBD2023-16)
  • Received Date: 2024-12-10
  • Rev Recd Date: 2025-04-09
  • Available Online: 2025-04-29
  • Publish Date: 2025-07-22
  •   Objective  Audio-visual Generalized Zero-Shot Learning (GZSL) integrates audio and visual signals in videos to enable the classification of known classes and the effective recognition of unseen classes. Most existing approaches prioritize the alignment of audio-visual and textual label embeddings, but overlook the interdependence between audio and video, and the mismatch between model outputs and target distributions. This study proposes an audio-visual GZSL method based on a Multimodal Fusion Transformer (MFT) to address these limitations.  Methods  The MFT employs a transformer-based multi-head attention mechanism to enable effective cross-modal interaction between visual and audio features. To optimize the output probability distribution, the Kullback-Leibler (KL) divergence between the predicted and target distributions is minimized, thereby aligning predictions more closely with the true distribution. This optimization also reduces overfitting and improves generalization to unseen classes. In addition, cosine similarity loss is applied to measure the similarity of learned representations within the same class, promoting feature consistency and improving discriminability.  Results and Discussions  The experiments include both GZSL and Zero-Shot Learning (ZSL) tasks. The ZSL task requires classification of unseen classes only, whereas the GZSL task addresses both unseen and seen class classification to mitigate catastrophic forgetting. To evaluate the proposed method, experiments are conducted on three benchmark datasets: VGGSound-GZSLcls, UCF-GZSLcls, and ActivityNet-GZSLcls (Table 1). MFT is quantitatively compared with five ZSL methods and nine GZSL methods (Table 2). The results show that the proposed method achieves state-of-the-art performance on all three datasets. For example, on ActivityNet-GZSLcls, MFT exceedes the previous best ClipClap-GZSL method by 14.6%. This confirms the effectiveness of MFT in modeling cross-modal dependencies, aligning predicted and target distributions, and achieving semantic consistency between audio and visual features. Ablation studies (Tables 35) further support the contribution of each module in the proposed framework.  Conclusions  This study proposes a transformer-based audio-visual GZSL method that uses a multi-head self-attention mechanism to extract intrinsic information from audio and video data and enhance cross-modal interaction. This design enables more accurate capture of semantic consistency between modalities, improving the quality of cross-modal feature representations. To align the predicted and target distributions and reinforce intra-class consistency, KL divergence and cosine similarity loss are incorporated during training. KL divergence improves the match between predicted and true distributions, while cosine similarity loss enhances discriminability within each class. Extensive experiments demonstrate the effectiveness of the proposed method.
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