mlsanityData to 7 Oct 2026

Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors

arXiv 2103.04814 ↗First posted 8 Mar 2021cs.CVcited by 9

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Abstract (arXiv)

Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learning global representations for image-level classification tasks instead of discriminative local region representations, which limits their transferability to region-level downstream tasks, such as object detection. To improve the transferability of pre-trained features to object detection, we present Deeply Unsupervised Patch Re-ID (DUPR), a simple yet effective method for unsupervised visual representation learning. The patch Re-ID task treats individual patch as a pseudo-identity and contrastively learns its correspondence in two views, enabling us to obtain discriminative local features for object detection. Then the proposed patch Re-ID is performed in a deeply unsupervised manner, appealing to object detection, which usually requires multilevel feature maps. Extensive experiments demonstrate that DUPR outperforms state-of-the-art unsupervised pre-trainings and even the ImageNet supervised pre-training on various downstream tasks related to object detection.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 51 of its 67 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

51 of 67 references.

References · 51

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  6. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  7. 2017Mask R-CNNcited by 7,259
  8. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  10. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  11. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  12. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
38 more
  1. 2014Going Deeper with Convolutionscited by 4,231
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  3. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  4. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  5. 2016Pyramid Scene Parsing Networkcited by 2,472
  6. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  7. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  8. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  9. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  10. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  11. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  12. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  13. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  14. 2019Contrastive Multiview Codingcited by 1,297
  15. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  16. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  17. 2016Colorful Image Colorizationcited by 1,113
  18. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
  19. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,062
  20. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,020
  21. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  22. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 806
  23. 2018Path Aggregation Network for Instance Segmentationcited by 796
  24. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  25. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 672
  26. 2020Dense Contrastive Learning for Self-Supervised Visual Pre-Trainingcited by 512
  27. 2018Rethinking ImageNet Pre-trainingcited by 376
  28. 2020Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learningcited by 297
  29. 2019Learning Correspondence from the Cycle-Consistency of Timecited by 269
  30. 2019Large Scale Adversarial Representation Learningcited by 238
  31. 2020What Should Not Be Contrastive in Contrastive Learningcited by 224
  32. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
  33. 2019Local Aggregation for Unsupervised Learning of Visual Embeddingscited by 204
  34. 2019Scaling and Benchmarking Self-Supervised Visual Representation Learningcited by 201
  35. 2020Unsupervised Learning of Dense Visual Representationscited by 147
  36. 2021Instance Localization for Self-supervised Detection Pretrainingcited by 99
  37. 2019Unsupervised Deep Trackingcited by 74
  38. 2020Learning Representations by Predicting Bags of Visual Wordscited by 73

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Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 2; 2025: 1.

9 papers.

  1. 2022Location-Aware Self-Supervised Transformers for Semantic Segmentationcited by 20
  2. 2025Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?cited by 19
  3. 2023Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequencescited by 9
  4. 2023FLSL: Feature-level Self-supervised Learningcited by 9
  5. 2023INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fieldscited by 6
  6. 2023GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentationcited by 3
  7. 2024Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Informationcited by 2
  8. 2024Self-Supervised Learning for Real-World Object Detection: a Surveycited by 2
  9. 2023Aligned Unsupervised Pretraining of Object Detectors with Self-trainingcited by 0