论文标题
挑战报告：Vipriors Action识别挑战
Challenge report:VIPriors Action Recognition Challenge
论文作者
论文摘要
本文是我们提交给Vipriors Action识别挑战的简短报告。行动认可吸引了许多研究人员的全面应用，但仍然具有挑战性。在本文中，我们研究了以前的方法并提出了我们的方法。在我们的方法中，我们主要是在慢速网络上进行改进，并与TSM融合以取得进一步的突破。另外，我们使用一种快速但有效的方法通过使用残留帧作为输入来从视频中提取运动功能。可以使用带有慢速慢速的残留框架来提取更好的运动功能，而剩余框架输入路径是现有RGB框架输入模型的绝佳补充。以及通过将3D卷积（SlowFast）与2D卷积（TSM）相结合而获得的更好性能。上述实验均在UCF101上从头开始训练。
This paper is a brief report to our submission to the VIPriors Action Recognition Challenge. Action recognition has attracted many researchers attention for its full application, but it is still challenging. In this paper, we study previous methods and propose our method. In our method, we are primarily making improvements on the SlowFast Network and fusing with TSM to make further breakthroughs. Also, we use a fast but effective way to extract motion features from videos by using residual frames as input. Better motion features can be extracted using residual frames with SlowFast, and the residual-frame-input path is an excellent supplement for existing RGB-frame-input models. And better performance obtained by combining 3D convolution(SlowFast) with 2D convolution(TSM). The above experiments were all trained from scratch on UCF101.
