# Exploring Set Similarity for Dense Self-supervised Representation Learning

arXiv: https://arxiv.org/abs/2107.08712 · HTML: https://mlsanity.com/paper/2107.08712/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 52 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [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)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [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)
- [Rethinking ImageNet Pre-training](https://mlsanity.com/paper/1811.08883/) (2018)

## Built on by (most cited first, 29 of 29)

- [Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training](https://mlsanity.com/paper/2305.13689/) (2023)
- [TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2112.01515/) (2021)
- [Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training](https://mlsanity.com/paper/2303.00874/) (2023)
- [Contrastive Conditional Latent Diffusion for Audio-visual Segmentation](https://mlsanity.com/paper/2307.16579/) (2023)
- [Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining](https://mlsanity.com/paper/2405.20462/) (2024)
- [CrOC: Cross-View Online Clustering for Dense Visual Representation Learning](https://mlsanity.com/paper/2303.13245/) (2023)
- [FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning](https://mlsanity.com/paper/2310.16412/) (2023)
- [Multi-Label Self-Supervised Learning with Scene Images](https://mlsanity.com/paper/2308.03286/) (2023)
- [Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning](https://mlsanity.com/paper/2306.13337/) (2023)
- [Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision](https://mlsanity.com/paper/2404.15672/) (2024)
- [Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization](https://mlsanity.com/paper/2310.16391/) (2023)
- [Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision](https://mlsanity.com/paper/2402.08960/) (2024)
- [Demystifying Unsupervised Semantic Correspondence Estimation](https://mlsanity.com/paper/2207.05054/) (2022)
- [Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/2212.06486/) (2022)
- [Regularly Truncated M-estimators for Learning with Noisy Labels](https://mlsanity.com/paper/2309.00894/) (2023)
- [Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation](https://mlsanity.com/paper/2310.01680/) (2023)
- [Deciphering the Projection Head: Representation Evaluation Self-supervised Learning](https://mlsanity.com/paper/2301.12189/) (2023)
- [De-coupling and De-positioning Dense Self-supervised Learning](https://mlsanity.com/paper/2303.16947/) (2023)
- [Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond](https://mlsanity.com/paper/2407.14823/) (2024)
- [Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision](https://mlsanity.com/paper/2506.20850/) (2025)
- [MSR: Making Self-supervised learning Robust to Aggressive Augmentations](https://mlsanity.com/paper/2206.01999/) (2022)
- [Self-supervised Learning of Contextualized Local Visual Embeddings](https://mlsanity.com/paper/2310.00527/) (2023)
- [Self-Supervised Learning for Real-World Object Detection: a Survey](https://mlsanity.com/paper/2410.07442/) (2024)
- [Pattern Integration and Enhancement Vision Transformer for Self-Supervised Learning in Remote Sensing](https://mlsanity.com/paper/2411.06091/) (2024)
- [Correlation between Alignment-Uniformity and Performance of Dense Contrastive Representations](https://mlsanity.com/paper/2210.08819/) (2022)
- [Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning](https://mlsanity.com/paper/2211.13844/) (2022)
- [Removing supervision in semantic segmentation with local-global matching and area balancing](https://mlsanity.com/paper/2303.17410/) (2023)
- [Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work](https://mlsanity.com/paper/2306.01929/) (2023)
- [Tracing Copied Pixels and Regularizing Patch Affinity in Copy Detection](https://mlsanity.com/paper/2602.17484/) (2026)

## Abstract (arXiv)

By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks. However, the pixel-level correspondence tends to be noisy because of many similar misleading pixels, e.g., backgrounds. To address this issue, in this paper, we propose to explore set similarity (SetSim) for dense self-supervised representation learning. We generalize pixel-wise similarity learning to set-wise one to improve the robustness because sets contain more semantic and structure information. Specifically, by resorting to attentional features of views, we establish corresponding sets, thus filtering out noisy backgrounds that may cause incorrect correspondences. Meanwhile, these attentional features can keep the coherence of the same image across different views to alleviate semantic inconsistency. We further search the cross-view nearest neighbours of sets and employ the structured neighbourhood information to enhance the robustness. Empirical evaluations demonstrate that SetSim is superior to state-of-the-art methods on object detection, keypoint detection, instance segmentation, and semantic segmentation.