Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation
This addresses a practical limitation in video representation learning for computer vision researchers, offering an incremental improvement to handle data scarcity and domain shifts.
The paper tackles the problem of poor generalization in self-supervised video representation learning when pretraining data is limited or domain differences exist, by proposing an auxiliary pretraining phase with similarity-based knowledge distillation (auxSKD) and a novel Video Segment Pace Prediction (VSPP) pretext task. The method achieves superior results on UCF101 and HMDB51 datasets when pretraining on Kinetics-100 compared to state-of-the-art methods, and improves existing methods when added as an extra phase.
Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. To mitigate these issues, we propose a novel approach to complement self-supervised pretraining via an auxiliary pretraining phase, based on knowledge similarity distillation, auxSKD, for better generalisation with a significantly smaller amount of video data, e.g. Kinetics-100 rather than Kinetics-400. Our method deploys a teacher network that iteratively distills its knowledge to the student model by capturing the similarity information between segments of unlabelled video data. The student model meanwhile solves a pretext task by exploiting this prior knowledge. We also introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which requires our model to predict the playback speed of a randomly selected segment of the input video to provide more reliable self-supervised representations. Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when added as an extra pretraining phase to recent state of the art self-supervised methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101 and HMDB51. Our code is available at https://github.com/Plrbear/auxSKD.