Composable Augmentation Encoding for Video Representation Learning

• URL: http://arxiv.org/abs/2104.00616v1
• Date: Thu, 1 Apr 2021 16:48:53 GMT
• Title: Composable Augmentation Encoding for Video Representation Learning
• Authors: Chen Sun, Arsha Nagrani, Yonglong Tian and Cordelia Schmid
• Abstract summary: We focus on contrastive methods for self-supervised video representation learning. A common paradigm in contrastive learning is to construct positive pairs by sampling different data views for the same instance, with different data instances as negatives. We propose an 'augmentation aware' contrastive learning framework, where we explicitly provide a sequence of augmentation parameterisations. We show that our method encodes valuable information about specified spatial or temporal augmentation, and in doing so also achieve state-of-the-art performance on a number of video benchmarks.
• Score: 94.2358972764708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We focus on contrastive methods for self-supervised video representation learning. A common paradigm in contrastive learning is to construct positive pairs by sampling different data views for the same instance, with different data instances as negatives. These methods implicitly assume a set of representational invariances to the view selection mechanism (eg, sampling frames with temporal shifts), which may lead to poor performance on downstream tasks which violate these invariances (fine-grained video action recognition that would benefit from temporal information). To overcome this limitation, we propose an 'augmentation aware' contrastive learning framework, where we explicitly provide a sequence of augmentation parameterisations (such as the values of the time shifts used to create data views) as composable augmentation encodings (CATE) to our model when projecting the video representations for contrastive learning. We show that representations learned by our method encode valuable information about specified spatial or temporal augmentation, and in doing so also achieve state-of-the-art performance on a number of video benchmarks.

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