目标检测中的不平衡问题及解决方案论文汇总

转载自https://github.com/kemaloksuz/ObjectDetectionImbalance

A Repository of the Papers Addressing Imbalance Problems in Object Detection

This repository provides an up-to-date the list of studies addressing imbalance problems in object detection. It follows the taxonomy provided in the following paper(please cite the paper if you benefit from this repository):

K. Oksuz, B. C. Cam, S. Kalkan, E. Akbas, “Imbalance Problems in Object Detection: A Review”, (under review), 2019.[preprint]

Table of Contents (Follows the taxonomy in the paper)

1. Class Imbalance
   1.1 Foreground-Backgorund Class Imbalance
   1.2 Foreground-Foreground Class Imbalance
2. Scale Imbalance
   2.1 Object/box-level Scale Imbalance
   2.2 Feature-level Imbalance
3. Spatial Imbalance
   3.1 Imbalance in Regression Loss
   3.2 IoU Distribution Imbalance
   3.3 Object Location Imbalance
4. Objective Imbalance

1. Class Imbalance

1.1. Foreground-Backgorund Class Imbalance

• Hard Sampling Methods
  • Random Sampling
  • Hard Example Mining
    • Bootstrapping, NeurIPS 1996, [paper]
    • SSD, ECCV 2016, [paper]
    • Online Hard Example Mining, CVPR 2016, [paper]
    • IoU-based Sampling, CVPR 2019, [paper]
  • Limit Search Space
    • Two-stage Object Detectors
    • IoU-lower Bound, ICCV 2015, [paper]
    • Objectness Prior, CVPR 2017, [paper]
    • Negative Anchor Filtering, CVPR 2018, [paper]
    • Objectness Module, ICCV 2019, [paper]
• Soft Sampling Methods
  • Focal Loss, ICCV 2017, [paper]
  • Gradient Harmonizing Mechanism, AAAI 2019, [paper]
  • Prime Sample Attention, arXiv 2019, [paper]
• Sampling-Free Methods
  • Is Sampling Heuristics Necessary in Training Deep Object Detectors?, arXiv 2019, [paper]
  • Residual Objectness for Imbalance Reduction, arXiv 2019, [paper]
  • AP Loss, CVPR 2019, [paper]
  • DR Loss, arXiv 2019, [paper]
• Generative Methods
  • Adversarial Faster-RCNN, CVPR 2017, [paper]
  • Task Aware Data Synthesis, CVPR 2019, [paper]
  • PSIS, arXiv 2019, [paper]
  • Bounding Box Generator, WACV 2020, [paper]

1.2. Foreground-Foreground Class Imbalance

• Fine-tuning Long Tail Distribution for Obj.Det., CVPR 2016, [paper]
• PSIS, arXiv 2019, [paper]
• OFB Sampling, WACV 2020, [paper]

2. Scale Imbalance

2.1. Object/box-level Scale Imbalance

• Methods Predicting from the Feature Hierarchy of Backbone Features

  • Scale-dependent Pooling, CVPR 2016, [paper]
  • SSD, ECCV 2016, [paper]
  • Multi Scale CNN, ECCV 2016, [paper]
  • Scale Aware Fast R-CNN, IEEE Transactions on Multimedia, 2018 [paper]

• Methods Based on Feature Pyramids

  • FPN, CVPR 2017, [paper]
  • See feature-level imbalance methods

• Methods Based on Image Pyramids

  • SNIP, CVPR 2018, [paper]
  • SNIPER, NeurIPS 2018, [paper]

• Methods Combining Image and Feature Pyramids

  • Efficient Featurized Image Pyramids, CVPR 2019, [paper]
  • Enriched Feature Guided Refinement Network, ICCV 2019, [paper]
  • Super-Resolution for Small Objects, ICCV 2019, [paper]
  • Scale Aware Trident Network, ICCV 2019, [paper]

2.2. Feature-level Imbalance

• Methods Using Pyramidal Features as a Basis

  • PANet, CVPR 2018, [paper]
  • Libra FPN, CVPR 2019, [paper]

• Methods Using Backbone Features as a Basis

  • STDN, CVPR 2018, [paper]
  • Parallel-FPN, ECCV 2018, [paper]
  • Deep Feature Pyramid Reconfiguration, ECCV 2018, [paper]
  • Zoom Out-and-In, IJCV 2019, [paper]
  • Multi-level FPN, AAAI 2019, [paper]
  • NAS-FPN, CVPR 2019, [paper]
  • Auto-FPN, ICCV 2019, [paper]

3. Spatial Imbalance

3.1. Imbalance in Regression Loss

• Lp norm based

  • Smooth L1, ICCV 2015, [paper]
  • Balanced L1, CVPR 2019, [paper]
  • KL Loss, CVPR 2019, [paper]
  • Gradient Harmonizing Mechanism, AAAI 2019, [paper]

• IoU based

  • IoU Loss, ACM IMM 2016, [paper]
  • Bounded IoU Loss, CVPR 2018, [paper]
  • Generalized IoU Loss, CVPR 2019, [paper]
  • Distance IoU Loss, AAAI 2020, [paper]
  • Complete IoU Loss, AAAI 2020, [paper]

3.2. IoU Distribution Imbalance

• Cascade R-CNN, CVPR 2018, [paper]
• Hierarchical Shot Detector, ICCV 2019, [paper]

3.3. Object Location Imbalance

• Guided Anchoring, CVPR 2019, [paper]
• FreeAnchor, NeurIPS 2019, [paper]

4. Objective Imbalance

• Task Weighting
• Classification Aware Regression Loss, arXiv 2019, [paper]
• Guided Loss, arXiv 2019, [paper]