In this report, we present the technical details of our submission to the 2022 EPIC-Kitchens Unsupervised Domain Adaptation (UDA) Challenge. Existing UDA methods align the global features extracted from the whole video clips across the source and target domains but suffer from the spatial redundancy of feature matching in video recognition. Motivated by the observation that in most cases a small image region in each video frame can be informative enough for the action recognition task, we propose to exploit informative image regions to perform efficient domain alignment. Specifically, we first use lightweight CNNs to extract the global information of the input two-stream video frames and select the informative image patches by a differentiable interpolation-based selection strategy. Then the global information from videos frames and local information from image patches are processed by an existing video adaptation method, i.e., TA3N, in order to perform feature alignment for the source domain and the target domain. Our method (without model ensemble) ranks 4th among this year's teams on the test set of EPIC-KITCHENS-100.

本研究介绍了一种利用信息图像区域来实现有效领域对齐的方法，并在2022 EPIC-Kitchens无监督领域适应（UDA）挑战中取得了第四名的成绩。该方法使用轻量级CNN提取输入双流视频帧的全局信息，并通过One Shot内容选择方法选择有信息量的图像块来进行特征对齐。

2022行动识别无监督领域自适应挑战赛: HNU-FPV队技术报告