# CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers

arXiv: https://arxiv.org/abs/2403.07700 · HTML: https://mlsanity.com/paper/2403.07700/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 41 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 30 of 31)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Localizing Objects with Self-Supervised Transformers and no Labels](https://mlsanity.com/paper/2109.14279/) (2021)
- [Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation](https://mlsanity.com/paper/1803.11365/) (2018)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Cut and Learn for Unsupervised Object Detection and Instance Segmentation](https://mlsanity.com/paper/2301.11320/) (2023)
- [TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut](https://mlsanity.com/paper/2209.00383/) (2022)
- [FreeSOLO: Learning to Segment Objects without Annotations](https://mlsanity.com/paper/2202.12181/) (2022)
- [DETReg: Unsupervised Pretraining with Region Priors for Object Detection](https://mlsanity.com/paper/2106.04550/) (2021)
- [Toward unsupervised, multi-object discovery in large-scale image collections](https://mlsanity.com/paper/2007.02662/) (2020)

## Built on by (most cited first, 16 of 16)

- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [Scene-Centric Unsupervised Panoptic Segmentation](https://mlsanity.com/paper/2504.01955/) (2025)
- [CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation](https://mlsanity.com/paper/2411.16319/) (2024)
- [Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection](https://mlsanity.com/paper/2510.18437/) (2025)
- [FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching](https://mlsanity.com/paper/2505.13174/) (2025)
- [MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling](https://mlsanity.com/paper/2409.02846/) (2024)
- [Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning](https://mlsanity.com/paper/2505.02071/) (2025)
- [Scene-Centric Unsupervised Video Panoptic Segmentation](https://mlsanity.com/paper/2606.04925/) (2026)
- [Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentation](https://mlsanity.com/paper/2606.15590/) (2026)
- [Enhancing Object Discovery for Unsupervised Instance Segmentation and Object Detection](https://mlsanity.com/paper/2508.02386/) (2025)
- [Unsupervised Instance Segmentation with Superpixels](https://mlsanity.com/paper/2509.05352/) (2025)
- [S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation](https://mlsanity.com/paper/2512.14440/) (2025)
- [Reference-based Category Discovery: Unsupervised Object Detection with Category Awareness](https://mlsanity.com/paper/2605.04606/) (2026)
- [VVitCutLER: Towards Unsupervised Object Detection and Segmentation in Videos](https://mlsanity.com/paper/2605.17584/) (2026)
- [Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision](https://mlsanity.com/paper/2609.06491/) (2026)
- [ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes](https://mlsanity.com/paper/2609.36891/) (2026)

## Abstract (arXiv)

In this paper, we introduce VoteCut, an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut employs normalized-cut based graph partitioning, clustering and a pixel voting approach. Additionally, We present CuVLER (Cut-Vote-and-LEaRn), a zero-shot model, trained using pseudo-labels, generated by VoteCut, and a novel soft target loss to refine segmentation accuracy. Through rigorous evaluations across multiple datasets and several unsupervised setups, our methods demonstrate significant improvements in comparison to previous state-of-the-art models. Our ablation studies further highlight the contributions of each component, revealing the robustness and efficacy of our approach. Collectively, VoteCut and CuVLER pave the way for future advancements in image segmentation.