mlsanityData to 7 Oct 2026

Contrast R-CNN for Continual Learning in Object Detection

arXiv 2108.04224 ↗First posted 11 Jul 2021cs.CVcited by 2

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Abstract (arXiv)

The continual learning problem has been widely studied in image classification, while rare work has been explored in object detection. Some recent works apply knowledge distillation to constrain the model to retain old knowledge, but this rigid constraint is detrimental for learning new knowledge. In our paper, we propose a new scheme for continual learning of object detection, namely Contrast R-CNN, an approach strikes a balance between retaining the old knowledge and learning the new knowledge. Furthermore, we design a Proposal Contrast to eliminate the ambiguity between old and new instance to make the continual learning more robust. Extensive evaluation on the PASCAL VOC dataset demonstrates the effectiveness of our approach.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 35 references that are in the map. First posted on arXiv 11 Jul 2021.

Builds on

28 of 35 references.

References · 28

  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
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  11. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  12. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
16 more
  1. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  2. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  3. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  4. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  5. 2017Focal Loss for Dense Object Detectioncited by 749
  6. 2019Large Scale Incremental Learningcited by 727
  7. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 711
  8. 2018Deformable ConvNets v2: More Deformable, Better Resultscited by 666
  9. 2018End-to-End Incremental Learningcited by 596
  10. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  11. 2020A Simple Semi-Supervised Learning Framework for Object Detectioncited by 282
  12. 2014Learning with Pseudo-Ensemblescited by 241
  13. 2017Incremental Learning of Object Detectors without Catastrophic Forgettingcited by 208
  14. 2021Instant-Teaching: An End-to-End Semi-Supervised Object Detection Frameworkcited by 112
  15. 2018Large Margin Deep Networks for Classificationcited by 110
  16. 2020Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNNcited by 61

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2025RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detectioncited by 5
  2. 2023COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Trackingcited by 4