{"id":1936,"date":"2026-04-02T17:04:01","date_gmt":"2026-04-02T09:04:01","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=1936"},"modified":"2026-05-16T17:25:11","modified_gmt":"2026-05-16T09:25:11","slug":"daufomer-a-deep-adaptive-uncertainty-driven-transformer-for-action-recognition","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=daufomer-a-deep-adaptive-uncertainty-driven-transformer-for-action-recognition","title":{"rendered":"DAUfomer: A Deep Adaptive Uncertainty-Driven Transformer for Action Recognition"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 29, Issue 4<\/a><\/p>\n<\/div>\n\n\n\n

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Yingcun Wang1<\/sup>, Rong Wang2<\/sup><\/i><\/a>, Shizhong Liu3<\/sup>, and Zhi Zeng4<\/sup><\/p>\n\n\n\n

1<\/sup>School of Information Engineering, Weifang Vocational College, Weifang 261031, China<\/p>\n\n\n\n

2<\/sup>School of International Business, Weifang Vocational College, Weifang 262737, China<\/p>\n\n\n\n

3<\/sup>Weifang Vocational College, Weifang 261031, China<\/p>\n\n\n\n

4<\/sup>Shandong Qiaotong Tianxia Network Technology Co., Ltd, Weifang, 261061, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0May 2, 2025
Accepted:\u00a0July13, 2025
Publication Date:\u00a0April 2, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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The overall framework of DAUfomer, containing multi-granularity feature extraction, uncertainty-driven spatial-temporal aggregation, proactive semantic enhancement<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| https:\/\/doi.org\/10.6180\/jase.202604_29(4).0009<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Current action recognition research excessively relies on deterministic feature correlation mechanisms, struggling to address core challenges including spatiotemporal heterogeneity, action category ambiguity, and cross-frame semantic discontinuity prevalent in video stream data. To this end, this study proposes the Deep Robust Recognition Transformer (DAUfomer), to reconstruct action recognition paradigms through three synergistic modules. Multi-granularity feature extraction module employs Transformer with dual attentions to extract low-dimensional and high-information-density spatiotemporal features from high-dimensional video streams, preserving local motion details while establishing global contextual correlations. Uncertainty-driven spatial-temporal aggregation module innovatively constructs a hybrid Gaussian-Dirichlet distribution model, transforming deterministic spatiotemporal attention into a probabilistic learnable Bayesian network. This enables dynamic adaptation to data distribution shifts through latent space uncertainty quantification. Proactive semantic enhancement architecture breaks traditional causal constraints in temporal modeling by designing a bidirectional temporal distillation mechanism. It leverages latent semantic cues from future frames to construct cross-frame attention correlation graphs, enhancing current action features via gated recurrent unit-based spatiotemporal context refinement. Finally, extensive results on two real world datasets, especially on the MSDanceAction dataset with a 4.48% ACC improvement compared to the second-best result, verify that DAUfomer conducts a new standard baseline in the action recognition task.<\/p>\n\n\n\n

Keywords:\u00a0Multimedia action recognition; probability-driven aggregation; proactive semantic enhancement<\/em><\/p>\n\n\n\n

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