mlsanityData to 7 Oct 2026

A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation

arXiv 2209.09341 ↗First posted 19 Sep 2022cs.CVcited by 15

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Abstract (arXiv)

We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only relies on independent image features and optical flows, which can be obtained using off-the-shelf self-supervised methods. It scales with the length of the sequence with no need for superpixels or sparsification, and it generalizes to different datasets without any specific training. This objective function can actually be derived from a form of spectral clustering applied to the entire video. Our method achieves on-par performance with the state of the art on standard benchmarks (DAVIS2016, SegTrack-v2, FBMS59), while being conceptually and practically much simpler. Code is available at https://ponimatkin.github.io/ssl-vos.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 60 of its 95 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

60 of 95 references.

References · 60

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38 more
  1. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  2. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,610
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  12. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
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  14. 2020How Well Do Self-Supervised Models Transfer?cited by 213
  15. 2022Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cutcited by 166
  16. 2016Learning Video Object Segmentation from Static Imagescited by 151
  17. 2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
  18. 2021Self-supervised Video Object Segmentation by Motion Groupingcited by 147
  19. 2020See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networkscited by 138
  20. 2020STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videoscited by 121
  21. 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
  22. 2017Online Adaptation of Convolutional Neural Networks for Video Object Segmentationcited by 109
  23. 2017Video Object Segmentation Without Temporal Informationcited by 93
  24. 2017SegFlow: Joint Learning for Video Object Segmentation and Optical Flowcited by 92
  25. 2020Zero-Shot Video Object Segmentation via Attentive Graph Neural Networkscited by 84
  26. 2021Full-Duplex Strategy for Video Object Segmentationcited by 78
  27. 2017FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videoscited by 77
  28. 2019Unsupervised Moving Object Detection via Contextual Information Separationcited by 72
  29. 2016It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videoscited by 71
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  33. 2021Do Different Tracking Tasks Require Different Appearance Models?cited by 62
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  35. 2018Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagationcited by 56
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Citing papers per year: 2022: 1; 2023: 8; 2024: 4; 2025: 2.

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  1. 2022Unsupervised Object Localization: Observing the Background to Discover Objectscited by 66
  2. 2023Self-supervised Object-Centric Learning for Videoscited by 49
  3. 2024Moving Object Segmentation: All You Need Is SAM (and Flow)cited by 30
  4. 2025Learning segmentation from point trajectoriescited by 14
  5. 2023Weakly-supervised Contrastive Learning for Unsupervised Object Discoverycited by 12
  6. 2023Appearance-Based Refinement for Object-Centric Motion Segmentationcited by 11
  7. 2023LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingcited by 8
  8. 2023You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentationcited by 6
  9. 2025Slot-BERT: Self-supervised Object Discovery in Surgical Videocited by 4
  10. 2023FODVid: Flow-guided Object Discovery in Videoscited by 3
  11. 2023Segmenting the motion components of a video: A long-term unsupervised modelcited by 2
  12. 2024Deep Spectral Improvement for Unsupervised Image Instance Segmentationcited by 1
3 more
  1. 2023Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slotscited by 0
  2. 2024Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentationcited by 0
  3. 2024Improving Weakly-supervised Video Instance Segmentation by Leveraging Spatio-temporal Consistencycited by 0