Abstract
Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by constructing surrogate supervision signals from the unlabeled data. However, some of the current methods tend to suffer from a background cheating problem, i.e., the prediction is highly dependent on the video background instead of the motion, making the model vulnerable to background changes. To alleviate the problem, we propose to remove the background impact by adding the background. That is, given a video, we randomly select a static frame and add it to every other frames to construct a distracting video sample. Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes. In addition, in order to prevent the static frame from disturbing the motion area too much, we restrict the feature being consistent with the temporally flipped feature of the reversed video, forcing the model to concentrate more on the motion. We term our method as Temporal-sensitive Background Erasing (TBE). Experiments on UCF101 and HMDB51 show that TBE brings about 6.4% and 4.8% improvements over the state-of-the-art method on the HMDB51 and UCF101 datasets respectively. And it is worth noting that the implementation of our method is so simple and neat and can be added as an additional regularization term to most of the SOTA methods without much efforts.
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URL
https://arxiv.org/abs/2009.05769
