Abstract
Human action recognition has become an important research focus in computer vision due to the wide range of applications where it is used. 3D Resnet-based CNN models, particularly MC3, R3D, and R(2+1)D, have different convolutional filters to extract spatiotemporal features. This paper investigates the impact of reducing the captured knowledge from temporal data, while increasing the resolution of the frames. To establish this experiment, we created similar designs to the three originals, but with a dropout layer added before the final classifier. Secondly, we then developed ten new versions for each one of these three designs. The variants include special attention blocks within their architecture, such as convolutional block attention module (CBAM), temporal convolution networks (TCN), in addition to multi-headed and channel attention mechanisms. The purpose behind that is to observe the extent of the influence each of these blocks has on performance for the restricted-temporal models. The results of testing all the models on UCF101 have shown accuracy of 88.98% for the variant with multiheaded attention added to the modified R(2+1)D. This paper concludes the significance of missing temporal features in the performance of the newly created increased resolution models. The variants had different behavior on class-level accuracy, despite the similarity of their enhancements to the overall performance.
Abstract (translated)
人类行为识别由于其广泛的应用场景，已成为计算机视觉领域的重要研究焦点。基于3D Resnet的CNN模型（如MC3、R3D和R(2+1)D）通过不同的卷积滤波器来提取空间-时间特征。本文探讨了减少捕获的时间数据知识的影响，并增加帧分辨率的效果。为了进行这项实验，我们创建了与原始设计相似的设计，但在最终分类器之前添加了一个dropout层。其次，为每种设计方案开发了十个新版本，这些变体在其架构中加入了特殊的注意模块，如卷积块注意力模块（CBAM）、时间卷积网络（TCN），以及多头和通道注意机制。 引入这些模块的目的是观察它们在限制时间特征的新建高分辨率模型性能中的影响程度。所有模型在UCF101数据集上的测试结果表明，在修改后的R(2+1)D中添加了多头注意力机制的变体获得了88.98%的准确率。本文得出结论，缺失的时间特性对新创建的、增加分辨率的模型性能具有重要意义。尽管这些变体对其整体性能进行了类似的增强，但它们在类别级别的准确性上表现出不同的行为。
URL
https://arxiv.org/abs/2601.10854
