The video understanding capability of video recognition models has been significantly improved by the development of deep learning techniques and various video datasets available. However, video recognition models are still vulnerable to invisible perturbations, which limits the use of deep video recognition models in the real world. We present a new benchmark for the robustness of action recognition classifiers to general corruptions, and show that a supervised contrastive learning framework is effective in obtaining discriminative and stable video representations, and makes deep video recognition models robust to general input corruptions. Experiments on the action recognition task for corrupted videos show the high robustness of the proposed method on the UCF101 and HMDB51 datasets with various common corruptions.

This study is aimed at finding a suitable method for generating time-series data such as video clips or avatar motions from text stating multiple events. This paper addresses the generation of variable-length time-series data considering the order and variable duration of events stated in the text. Although the use of the variant of Mean Squared Error (MSE) is a common means of training, only the gap between the element of ground-truth (GT) data and generated data at the same time are considered. Thus, variants of MSE are unsuitable for the task at hand because the loss may not be small for the generated and GT data with the same order of events if the time for each event does not overlap. To solve the problem, we propose a Dynamic Time Warping-Like method for Variable-Length data (DTWL-VL), which determines the corresponding elements of the GT and the generated data, allowing for the time difference between them, and makes them closer. We compared DTWL-VL, a variant of MSE, and an existing method for time-series data generation which considers the time difference between the corresponding part in the GT and generated data. Since the existing method is aimed at generating fixed-length data, we extend the method for generating variable-length time-series data. We conducted experiments using a dataset prepared for this study. Both DTWL-VL and the existing methods outperformed the MSE variant. Moreover, although the existing method outperformed DTWL-VL under certain settings, DTWL-VL required a smaller training period.