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• W2902016181 abstract "To alleviate the expensive cost of data collection and annotation, manyself-supervised learning methods were proposed to learn image representationswithout human-labeled annotations. However, self-supervised learning for videorepresentations is not yet well-addressed. In this paper, we propose a novel3DConvNet-based fully self-supervised framework to learn spatiotemporal videofeatures without using any human-labeled annotations. First, a set ofpre-designed geometric transformations (e.g. rotating 0 degree, 90 degrees, 180degrees, and 270 degrees) are applied to each video. Then a pretext task can bedefined as recognizing the pre-designed geometric transformations. Therefore,the spatiotemporal video features can be learned in the process ofaccomplishing this pretext task without using human-labeled annotations. Thelearned spatiotemporal video representations can further be employed aspretrained features for different video-related applications. The proposedgeometric transformations (e.g. rotations) are proved to be effective to learnrepresentative spatiotemporal features in our 3DConvNet-based fullyself-supervised framework. With the pre-trained spatiotemporal features fromtwo large video datasets, the performance of action recognition issignificantly boosted up by 20.4% on UCF101 dataset and 16.7% on HMDB51 datasetrespectively compared to that from the model trained from scratch. Furthermore,our framework outperforms the state-of-the-arts of fully self-supervisedmethods on both UCF101 and HMDB51 datasets and achieves 62.9% and 33.7%accuracy respectively." @default.
• W2902016181 created "2018-12-11" @default.
• W2902016181 creator A5016194100 @default.
• W2902016181 creator A5074244244 @default.
• W2902016181 date "2018-11-28" @default.
• W2902016181 modified "2023-09-26" @default.
• W2902016181 title "Self-supervised Spatiotemporal Feature Learning by Video Geometric Transformations" @default.
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