# Dense Semantic Contrast for Self-Supervised Visual Representation Learning

arXiv: https://arxiv.org/abs/2109.07756 · HTML: https://mlsanity.com/paper/2109.07756/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 49 references that are in the map.
- First posted on arXiv 16 Sep 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)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (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)
- [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)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2011.10043/) (2020)
- [Local Aggregation for Unsupervised Learning of Visual Embeddings](https://mlsanity.com/paper/1903.12355/) (2019)
- [Unsupervised Learning of Dense Visual Representations](https://mlsanity.com/paper/2011.05499/) (2020)
- [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://mlsanity.com/paper/2005.10977/) (2020)
- [Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning](https://mlsanity.com/paper/2006.11476/) (2020)
- [Online Deep Clustering for Unsupervised Representation Learning](https://mlsanity.com/paper/2006.10645/) (2020)
- [Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning](https://mlsanity.com/paper/2001.00294/) (2020)
- [Unsupervised Deep Learning by Neighbourhood Discovery](https://mlsanity.com/paper/1904.11567/) (2019)
- [Multi-View Correlation Distillation for Incremental Object Detection](https://mlsanity.com/paper/2107.01787/) (2021)
- [GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering](https://mlsanity.com/paper/2002.11863/) (2020)
- [Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning](https://mlsanity.com/paper/1908.09990/) (2019)
- [Self-Training for Domain Adaptive Scene Text Detection](https://mlsanity.com/paper/2005.11487/) (2020)

## Built on by (most cited first, 24 of 24)

- [Self-Supervised Learning of Object Parts for Semantic Segmentation](https://mlsanity.com/paper/2204.13101/) (2022)
- [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)
- [Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition](https://mlsanity.com/paper/2207.11518/) (2022)
- [Contrastive Conditional Latent Diffusion for Audio-visual Segmentation](https://mlsanity.com/paper/2307.16579/) (2023)
- [Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2206.10711/) (2022)
- [Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation](https://mlsanity.com/paper/2203.06553/) (2022)
- [Weakly-supervised Contrastive Learning for Unsupervised Object Discovery](https://mlsanity.com/paper/2307.03376/) (2023)
- [Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/2212.06486/) (2022)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (2024)
- [Sound and Visual Representation Learning with Multiple Pretraining Tasks](https://mlsanity.com/paper/2201.01046/) (2022)
- [Representation Learning by Detecting Incorrect Location Embeddings](https://mlsanity.com/paper/2204.04788/) (2022)
- [ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment](https://mlsanity.com/paper/2111.12460/) (2021)
- [Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning](https://mlsanity.com/paper/2211.01796/) (2022)
- [Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning](https://mlsanity.com/paper/2502.05282/) (2025)
- [Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning](https://mlsanity.com/paper/2307.04651/) (2023)
- [Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision](https://mlsanity.com/paper/2506.20850/) (2025)
- [UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection](https://mlsanity.com/paper/2205.04683/) (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)
- [MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization](https://mlsanity.com/paper/2308.11351/) (2023)
- [Tracing Copied Pixels and Regularizing Patch Affinity in Copy Detection](https://mlsanity.com/paper/2602.17484/) (2026)
- [Masked Next-Scale Prediction for Self-supervised Scene Text Recognition](https://mlsanity.com/paper/2605.14885/) (2026)

## Abstract (arXiv)

Self-supervised representation learning for visual pre-training has achieved remarkable success with sample (instance or pixel) discrimination and semantics discovery of instance, whereas there still exists a non-negligible gap between pre-trained model and downstream dense prediction tasks. Concretely, these downstream tasks require more accurate representation, in other words, the pixels from the same object must belong to a shared semantic category, which is lacking in the previous methods. In this work, we present Dense Semantic Contrast (DSC) for modeling semantic category decision boundaries at a dense level to meet the requirement of these tasks. Furthermore, we propose a dense cross-image semantic contrastive learning framework for multi-granularity representation learning. Specially, we explicitly explore the semantic structure of the dataset by mining relations among pixels from different perspectives. For intra-image relation modeling, we discover pixel neighbors from multiple views. And for inter-image relations, we enforce pixel representation from the same semantic class to be more similar than the representation from different classes in one mini-batch. Experimental results show that our DSC model outperforms state-of-the-art methods when transferring to downstream dense prediction tasks, including object detection, semantic segmentation, and instance segmentation. Code will be made available.