# CrOC: Cross-View Online Clustering for Dense Visual Representation Learning

arXiv: https://arxiv.org/abs/2303.13245 · HTML: https://mlsanity.com/paper/2303.13245/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 56 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 30 of 44)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [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)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2011.10043/) (2020)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals](https://mlsanity.com/paper/2102.06191/) (2021)
- [Colorization as a Proxy Task for Visual Understanding](https://mlsanity.com/paper/1703.04044/) (2017)
- [PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering](https://mlsanity.com/paper/2103.17070/) (2021)
- [Efficient Visual Pretraining with Contrastive Detection](https://mlsanity.com/paper/2103.10957/) (2021)
- [Efficient Self-supervised Vision Transformers for Representation Learning](https://mlsanity.com/paper/2106.09785/) (2021)
- [Unsupervised Learning of Dense Visual Representations](https://mlsanity.com/paper/2011.05499/) (2020)
- [Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases](https://mlsanity.com/paper/2007.13916/) (2020)
- [VICRegL: Self-Supervised Learning of Local Visual Features](https://mlsanity.com/paper/2210.01571/) (2022)
- [Aligning Pretraining for Detection via Object-Level Contrastive Learning](https://mlsanity.com/paper/2106.02637/) (2021)

## Built on by (most cited first, 21 of 21)

- [Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations](https://mlsanity.com/paper/2308.11796/) (2023)
- [VideoMAC: Video Masked Autoencoders Meet ConvNets](https://mlsanity.com/paper/2402.19082/) (2024)
- [CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping](https://mlsanity.com/paper/2310.07855/) (2023)
- [Near, far: Patch-ordering enhances vision foundation models' scene understanding](https://mlsanity.com/paper/2408.11054/) (2024)
- [Object-Centric Pretraining via Target Encoder Bootstrapping](https://mlsanity.com/paper/2503.15141/) (2025)
- [MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning](https://mlsanity.com/paper/2506.08694/) (2025)
- [Exploring Structural Degradation in Dense Representations for Self-supervised Learning](https://mlsanity.com/paper/2510.17299/) (2025)
- [S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving](https://mlsanity.com/paper/2410.23085/) (2024)
- [ActiveDC: Distribution Calibration for Active Finetuning](https://mlsanity.com/paper/2311.07634/) (2023)
- [A Simple Framework for Open-Vocabulary Zero-Shot Segmentation](https://mlsanity.com/paper/2406.16085/) (2024)
- [CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation](https://mlsanity.com/paper/2503.09878/) (2025)
- [DIP: Unsupervised Dense In-Context Post-training of Visual Representations](https://mlsanity.com/paper/2506.18463/) (2025)
- [Semantic Concentration for Self-Supervised Dense Representations Learning](https://mlsanity.com/paper/2509.09429/) (2025)
- [Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancing](https://mlsanity.com/paper/2407.11168/) (2024)
- [Patch-Level Kernel Alignment for Dense Self-Supervised Learning](https://mlsanity.com/paper/2509.05606/) (2025)
- [Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning](https://mlsanity.com/paper/2509.11344/) (2025)
- [Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation](https://mlsanity.com/paper/2404.13505/) (2024)
- [Unsqueeze [CLS] Bottleneck to Learn Rich Representations](https://mlsanity.com/paper/2407.17671/) (2024)
- [Object-level Self-Distillation for Vision Pretraining](https://mlsanity.com/paper/2506.05409/) (2025)
- [Information theoretic underpinning of self-supervised learning by clustering](https://mlsanity.com/paper/2605.11870/) (2026)
- [Depth-Guided Contrastive Learning for 2D Representations with 3D Spatial Awareness](https://mlsanity.com/paper/2609.28159/) (2026)

## Abstract (arXiv)

Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view consistency objective with an Online Clustering mechanism (CrOC) to discover and segment the semantics of the views. In the absence of hand-crafted priors, the resulting method is more generalizable and does not require a cumbersome pre-processing step. More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other. We demonstrate excellent performance on linear and unsupervised segmentation transfer tasks on various datasets and similarly for video object segmentation. Our code and pre-trained models are publicly available at https://github.com/stegmuel/CrOC.