English(EN) Fine-Grained Action Recognition with Cross-Attentive Latent Sparse Experts

FineX方法通过新颖的融合技术推进细粒度动作识别

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-14 04:00

研究人员推出了一种新颖的细粒度人类动作识别方法FineX。该方法通过整合RGB外观、姿态热力图几何和骨骼图拓扑，有效地区分视觉上相似的动作。FineX利用成对交叉注意力在这些表示之间进行信息交换，并利用潜在稀疏专家混合（Mixture-of-Experts）将数据路由到相关专家。该方法在Gym99、Gym288和Diving48等基准数据集上展示了最先进的性能，显著提高了长尾Gym288数据集上的平均类别准确率。 AI

影响通过整合多样化的视觉线索，推进了细粒度动作识别，可能改进视频分析和人机交互等应用。

排序理由该项目是一篇研究论文，详细介绍了细粒度动作识别的新方法，包括其技术方法和基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Imtiaz Ul Hassan, Tasweer Ahmad, Nik Bessis, Ardhendu Behera · 2026-08-14 04:00

具有跨注意力潜在稀疏专家的细粒度动作识别

arXiv:2608.13458v1 Announce Type: new Abstract: Fine-grained human action recognition (FHAR) must distinguish visually similar actions that differ mainly in body configuration, timing, or local appearance. RGB representations retain visual context but often suppress joint-level g…

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具有跨注意力潜在稀疏专家的细粒度动作识别

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