mlsanityData to 7 Oct 2026

Cycle-Contrast for Self-Supervised Video Representation Learning

arXiv 2010.14810 ↗First posted 28 Oct 2020cs.CV cs.LGcited by 34

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Abstract (arXiv)

We present Cycle-Contrastive Learning (CCL), a novel self-supervised method for learning video representation. Following a nature that there is a belong and inclusion relation of video and its frames, CCL is designed to find correspondences across frames and videos considering the contrastive representation in their domains respectively. It is different from recent approaches that merely learn correspondences across frames or clips. In our method, the frame and video representations are learned from a single network based on an R3D architecture, with a shared non-linear transformation for embedding both frame and video features before the cycle-contrastive loss. We demonstrate that the video representation learned by CCL can be transferred well to downstream tasks of video understanding, outperforming previous methods in nearest neighbour retrieval and action recognition tasks on UCF101, HMDB51 and MMAct.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2020.

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Citing papers per year: 2021: 14; 2022: 10; 2023: 4; 2024: 4; 2025: 0; 2026: 2.

34 papers.

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  1. 2022Bridging Video-text Retrieval with Multiple Choice Questionscited by 79
  2. 2021Motion-aware Contrastive Video Representation Learning via Foreground-background Mergingcited by 45
  3. 2021PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learningcited by 41
  4. 2021Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learningcited by 34
  5. 2022MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrievalcited by 29
  6. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  7. 2021Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimizationcited by 27
  8. 2021Cross-modal Knowledge Distillation for Vision-to-Sensor Action Recognitioncited by 25
  9. 2022Motion-Focused Contrastive Learning of Video Representationscited by 23
  10. 2022C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentationcited by 23
  11. 2021Contrastive Learning of Image Representations with Cross-Video Cycle-Consistencycited by 19
  12. 2021Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentationcited by 19
22 more
  1. 2021Motion-Augmented Self-Training for Video Recognition at Smaller Scalecited by 17
  2. 2022Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistencycited by 15
  3. 2022Dual Contrastive Learning for Spatio-temporal Representationcited by 15
  4. 2021Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representationcited by 11
  5. 2023Self-Supervised Video Representation Learning via Latent Time Navigationcited by 11
  6. 2023Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequencescited by 9
  7. 2023Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learningcited by 7
  8. 2021Contrastive Learning of Global-Local Video Representationscited by 6
  9. 2021Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrastingcited by 6
  10. 2022Progressive Cross-modal Knowledge Distillation for Human Action Recognitioncited by 6
  11. 2021Inter-intra Variant Dual Representations forSelf-supervised Video Recognitioncited by 5
  12. 2024Contrastive Learning of Person-independent Representations for Facial Action Unit Detectioncited by 5
  13. 2023CycleCL: Self-supervised Learning for Periodic Videoscited by 4
  14. 2022Motion Aware Self-Supervision for Generic Event Boundary Detectioncited by 3
  15. 2021ParamCrop: Parametric Cubic Cropping for Video Contrastive Learningcited by 2
  16. 2024Learning Contrastive Feature Representations for Facial Action Unit Detectioncited by 2
  17. 2021Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Taskcited by 1
  18. 2022Temporal Contrastive Learning with Curriculumcited by 1
  19. 2022Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representationscited by 1
  20. 2024Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiencycited by 1
  21. 2026Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectivescited by 0
  22. 2026`Attention-Guided Cross-Temporal Clustering for Self-Supervised Video Object Segmentationcited by 0