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]
BibTeX entry:
@ARTICLE{imbalance,
author = {Kemal Oksuz and Baris Can Cam and Sinan Kalkan and Emre Akbas},
title = "{Imbalance Problems in Object Detection: A Review}",
journal = {arXiv e-prints},
year = "2019",
month = "Aug",
pages = {arXiv:1909.00169},
ee = {https://arxiv.org/abs/1909.00169},
eprint = {1909.00169}
}
How to request addition of a paper
If you know of a paper that addresses an imbalance problem concerning object detection and is not on this repository, you are welcome to request the addition of that paper by submitting a pull request. In your pull request please briefly state which section of your paper is related to which problem.
Table of Contents (Follows the taxonomy in the paper)
- Class Imbalance
1.1 Foreground-Backgorund Class Imbalance
1.2 Foreground-Foreground Class Imbalance - Scale Imbalance
2.1 Object/box-level Scale Imbalance
2.2 Feature-level Imbalance - Spatial Imbalance
3.1 Imbalance in Regression Loss
3.2 IoU Distribution Imbalance
3.3 Object Location Imbalance - Objective Imbalance
1. Class Imbalance
1.1. Foreground-Backgorund Class Imbalance
- Hard Sampling Methods
- Random Sampling
- Hard Example Mining
- Limit Search Space
- Soft Sampling Methods
- Generative Methods
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 (Under Review)
2. Scale Imbalance
2.1. Object/box-level Scale Imbalance
-
Methods Predicting from the Feature Hierarchy of Backbone Features
-
Methods Based on Feature Pyramids
- FPN, CVPR 2017, [paper]
- See feature-level imbalance methods
-
Methods Based on Image Pyramids
-
Methods Combining Image and Feature Pyramids
- Scale Aware Trident Network, arXiv 2019, [paper]
2.2. Feature-level Imbalance
-
Methods Using Pyramidal Features as a Basis
-
Methods Using Backbone Features as a Basis
3. Spatial Imbalance
3.1. Imbalance in Regression Loss
-
Lp norm based
-
IoU based
3.2. IoU Distribution Imbalance
- Cascade R-CNN, CVPR 2018, [paper]
3.3. Object Location Imbalance
- Guided Anchoring, CVPR 2019, [paper]
4. Objective Imbalance
- Task Weighting
- Classification Aware Regression Loss, arXiv 2019, [paper]
Contact
Please contact Kemal Öksüz ([email protected]) for your questions about this webpage.