Video Contrastive Learning with Global Context

Abstract

Contrastive learning has revolutionized self-supervised image representation learning field, and recently been adapted to video domain. One of the greatest advantages of contrastive learning is that it allows us to flexibly define powerful loss objectives as long as we can find a reasonable way to formulate positive and negative samples to contrast. However, existing approaches rely heavily on the short-range spatiotemporal salience to form clip-level contrastive signals, thus limit themselves from using global context. In this paper, we propose a new video-level contrastive learning method based on segments to formulate positive pairs. Our formulation is able to capture global context in a video, thus robust to temporal content change. We also incorporate a temporal order regularization term to enforce the inherent sequential structure of videos. Extensive experiments show that our video-level contrastive learning framework (VCLR) is able to outperform previous state-of-the-arts on five video datasets for downstream action classification, action localization and video retrieval. Code is available at https://github.com/amazon-research/ video-contrastive-learning.* Work done during an internship at Amazon.

Feature-Guided Multitask Change Detection Network

“…Contrastive methods learn representations in a discriminative manner by contrasting similar (positive) data pairs against dissimilar (negative) pairs [44]. Contrast learning is widely used in self-supervised representation learning [45][46][47]. Because there are no labels, a positive sample consists of an augmented image.…”

“…[46] proposed a pixel-to-pixel contrastive learning method for semantic segmentation, which is able to discover information across images and perform feature alignment on a small batch of data. Kuang et al [47] proposed a new video-level contrastive learning method based on segments to formulate positive pairs.…”