mlsanityData to 14 Oct 2026

Learning Correspondence from the Cycle-Consistency of Time

arXiv 1903.07593 ↗First posted 18 Mar 2019cs.CV cs.AI cs.LGcited by 269

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Abstract (arXiv)

We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch. At training time, our model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, we use the acquired representation to find nearest neighbors across space and time. We demonstrate the generalizability of the representation -- without finetuning -- across a range of visual correspondence tasks, including video object segmentation, keypoint tracking, and optical flow. Our approach outperforms previous self-supervised methods and performs competitively with strongly supervised methods.

Key facts. Cited by 269 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2019.

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Citing papers per year: 2021: 75; 2022: 69; 2023: 52; 2024: 30; 2025: 23; 2026: 20.

269 papers.

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  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 694
  4. 2023Emergent Correspondence from Image Diffusioncited by 446
  5. 2022Masked Autoencoders As Spatiotemporal Learnerscited by 429
  6. 2022Kubric: A scalable dataset generatorcited by 381
  7. 2023PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Trackingcited by 276
  8. 2022TAP-Vid: A Benchmark for Tracking Any Point in a Videocited by 263
  9. 2021Keeping Your Eye on the Ball: Trajectory Attention in Video Transformerscited by 224
  10. 2022Particle Video Revisited: Tracking Through Occlusions Using Point Trajectoriescited by 217
  11. 2021A Large-Scale Study on Unsupervised Spatiotemporal Representation Learningcited by 214
  12. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
38 more
  1. 2023Tracking Everything Everywhere All at Oncecited by 193
  2. 2021Reinforcement Learning with Prototypical Representationscited by 179
  3. 2021VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examplescited by 154
  4. 2021Self-Supervised Representation Learning: Introduction, Advances and Challengescited by 148
  5. 2021Towards Long-Form Video Understandingcited by 146
  6. 2022Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videoscited by 139
  7. 2021Layered Neural Atlases for Consistent Video Editingcited by 137
  8. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  9. 2021A Survey on Deep Learning Technique for Video Segmentationcited by 132
  10. 2024CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videoscited by 128
  11. 2024BootsTAP: Bootstrapped Training for Tracking-Any-Pointcited by 120
  12. 2023CoDeF: Content Deformation Fields for Temporally Consistent Video Processingcited by 108
  13. 2021Region Similarity Representation Learningcited by 96
  14. 2021Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspectivecited by 81
  15. 2023Siamese Masked Autoencoderscited by 76
  16. 2021Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Spacecited by 67
  17. 2024DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Videocited by 64
  18. 2021Do Different Tracking Tasks Require Different Appearance Models?cited by 62
  19. 2023MFT: Long-Term Tracking of Every Pixelcited by 62
  20. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 60
  21. 2022Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Cameracited by 57
  22. 2022Mix and Localize: Localizing Sound Sources in Mixturescited by 54
  23. 2021Distilling Audio-Visual Knowledge by Compositional Contrastive Learningcited by 53
  24. 2022CATs++: Boosting Cost Aggregation with Convolutions and Transformerscited by 53
  25. 2023Test of Time: Instilling Video-Language Models with a Sense of Timecited by 51
  26. 2021Learning the Predictability of the Futurecited by 49
  27. 2022Breaking the "Object" in Video Object Segmentationcited by 49
  28. 2022Dissecting Self-Supervised Learning Methods for Surgical Computer Visioncited by 48
  29. 2021The Emergence of Objectness: Learning Zero-Shot Segmentation from Videoscited by 45
  30. 2021Representation Learning via Global Temporal Alignment and Cycle-Consistencycited by 44
  31. 2024DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Controlcited by 44
  32. 2021Convolutional Hough Matching Networkscited by 43
  33. 2021Warp Consistency for Unsupervised Learning of Dense Correspondencescited by 42
  34. 2021PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learningcited by 41
  35. 2021Video Contrastive Learning with Global Contextcited by 41
  36. 2022textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistencycited by 41
  37. 2021Planar Surface Reconstruction from Sparse Viewscited by 40
  38. 2023Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled videocited by 40

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