In this article, we study the problem of video-based action recognition. We improve the action recognition performance by finding an effective temporal and appearance representation. For capturing the temporal representation, we introduce two temporal learning techniques for improving long-term temporal information modeling, specifically Temporal Relational Network and Temporal Second-Order Pooling-based Network. Moreover, we harness the representation using complementary learning techniques, specifically Global-Local Network and Fuse-Inception Network. Performance evaluation on three datasets (UCF101, HMDB-51, and Mini-Kinetics-200) demonstrated the superiority of the proposed framework compared to the 2D Deep ConvNets-based state-of-the-art techniques.

中文翻译：

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通过时间和互补学习提高动作识别

在本文中，我们研究了基于视频的动作识别问题。我们通过找到有效的时间和外观表示来提高动作识别性能。为了捕获时间表示，我们引入了两种时间学习技术来改进长期时间信息建模，特别是时间关系网络和基于时间二阶池的网络。此外，我们使用互补的学习技术来利用表示，特别是 Global-Local Network 和 Fuse-Inception Network。对三个数据集（UCF101、HMDB-51 和 Mini-Kinetics-200）的性能评估证明了与基于 2D Deep ConvNets 的最先进技术相比，所提出的框架的优越性。