mlsanityData to 7 Oct 2026

Unsupervised Object Segmentation by Redrawing

arXiv 1905.13539 ↗First posted 27 May 2019cs.CV cs.LG stat.MLcited by 74

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Abstract (arXiv)

Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided at pixel level, building such a dataset for any new domain can be very time-consuming. We present ReDO, a new model able to extract objects from images without any annotation in an unsupervised way. It relies on the idea that it should be possible to change the textures or colors of the objects without changing the overall distribution of the dataset. Following this assumption, our approach is based on an adversarial architecture where the generator is guided by an input sample: given an image, it extracts the object mask, then redraws a new object at the same location. The generator is controlled by a discriminator that ensures that the distribution of generated images is aligned to the original one. We experiment with this method on different datasets and demonstrate the good quality of extracted masks.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2019.

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Citing papers per year: 2021: 25; 2022: 22; 2023: 17; 2024: 7; 2025: 3.

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  1. 2021PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clusteringcited by 163
  2. 2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
  3. 2021Illiterate DALL-E Learns to Composecited by 147
  4. 2021GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinementcited by 109
  5. 2021ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentationcited by 93
  6. 2021Generalization and Robustness Implications in Object-Centric Learningcited by 81
  7. 2023Diffusion Models for Open-Vocabulary Segmentationcited by 63
  8. 2022Improving Object-centric Learning with Query Optimizationcited by 60
  9. 2022Unsupervised Salient Object Detection with Spectral Cluster Votingcited by 56
  10. 2022Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentationcited by 55
  11. 2021Labels4Free: Unsupervised Segmentation using StyleGANcited by 53
  12. 2023DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discoverycited by 53
38 more
  1. 2021Unsupervised Layered Image Decomposition into Object Prototypescited by 50
  2. 2021Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Modelscited by 44
  3. 2022Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motioncited by 42
  4. 2021TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentationcited by 38
  5. 2021Object-aware Contrastive Learning for Debiased Scene Representationcited by 34
  6. 2021Unsupervised Foreground Extraction via Deep Region Competitioncited by 26
  7. 2022Unsupervised Multi-View Object Segmentation Using Radiance Field Propagationcited by 22
  8. 2022Decoder Denoising Pretraining for Semantic Segmentationcited by 20
  9. 2022MOVE: Unsupervised Movable Object Segmentation and Detectioncited by 20
  10. 2021GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generationcited by 19
  11. 2022Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillationcited by 19
  12. 2022AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypointscited by 17
  13. 2022Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Imagescited by 16
  14. 2022Synthetic Data Supervised Salient Object Detectioncited by 15
  15. 2024DeSplat: Decomposed Gaussian Splatting for Distractor-Free Renderingcited by 15
  16. 2021Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularizationcited by 13
  17. 2022Inductive Biases for Object-Centric Representations in the Presence of Complex Texturescited by 13
  18. 2024DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentationcited by 13
  19. 2023Weakly-supervised Contrastive Learning for Unsupervised Object Discoverycited by 12
  20. 2022Unsupervised Multi-object Segmentation Using Attention and Soft-argmaxcited by 11
  21. 2021Contrastive Fine-grained Class Clustering via Generative Adversarial Networkscited by 9
  22. 2023Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Priorcited by 8
  23. 2023LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingcited by 8
  24. 2024ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentationcited by 8
  25. 2021Learning Foreground-Background Segmentation from Improved Layered GANscited by 7
  26. 2023PaintSeg: Training-free Segmentation via Paintingcited by 7
  27. 2023Optical Flow boosts Unsupervised Localization and Segmentationcited by 7
  28. 2023Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentationcited by 7
  29. 2022Unsupervised Tissue Segmentation via Deep Constrained Gaussian Networkcited by 6
  30. 2022Refine and Represent: Region-to-Object Representation Learningcited by 5
  31. 2022Foreground-Background Separation through Concept Distillation from Generative Image Foundation Modelscited by 5
  32. 2021Coherent, super resolved radar beamforming using self-supervised learningcited by 4
  33. 2021ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignmentcited by 4
  34. 2022K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentationcited by 4
  35. 2023SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTAcited by 4
  36. 2023Learning Neural Eigenfunctions for Unsupervised Semantic Segmentationcited by 4
  37. 2023SEMPART: Self-supervised Multi-resolution Partitioning of Image Semanticscited by 4
  38. 2023Benchmarking and Analysis of Unsupervised Object Segmentation from Real-world Single Imagescited by 4

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