SCVRL: Shuffled Contrastive Video Representation Learning
Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, Davide Modolo
20222022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)17 citationsDOI
Abstract
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.
Topics & Concepts
Computer scienceArtificial intelligencePretextFeature learningSemantics (computer science)Focus (optics)Natural language processingMachine learningContrast (vision)TransformerPoliticsLawProgramming languagePolitical scienceVoltageOpticsPhysicsQuantum mechanicsHuman Pose and Action RecognitionMultimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning