Improving corruption robustness of deep neural network in video classification - Gong, Zeyu (Nanyang Technological University, 2023)
Recently, video social media platforms grow fast and wide, video analysis become a necessary task. Deep neural networks have previously demonstrated exceptional performance on a variety of computer vision tasks, including image classification and action recognition. However, some studies prove that when the training video clips and test video clips are marginally different, the accuracy of the deep neural network can observably decrease. And corrupted test videos can lead to even worse test accuracy. The corruption robustness of a video classification model refers to its capability to maintain accurate predictions when presented with corrupted input data. The corruption can occur due to various factors such as weather, sensor noise and transmission errors. Therefore, the robustness of the video classification model is still a problem and there is a large progress to be made.