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feat: organize the papers and add ICCV 2023 papers
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Expand Up @@ -10,14 +10,27 @@ Papers and codes are listed.

## Contents

- [Vanilla](#vanilla)
- [Foreground Background](#foreground-background)
- [Heterogeneous](#heterogeneous)
- [Teacher Free](#teacher-free)
- [Knowledge Distillation for General Object Detectors](#knowledge-distillation-for-general-object-detectors)
- [Feature Distillation](#feature-distillation)
- [Foreground Masks](#foreground-masks)
- [Ground Truth Guided](#ground-truth-guided)
- [Prediction Guided](#prediction-guided)
- [Attention Guided](#attention-guided)
- [Miscellaneous Foreground Masks](#miscellaneous-foreground-masks)
- [Miscellaneous Feature Distillation](#miscellaneous-feature-distillation)
- [Instance Distillation](#instance-distillation)
- [Label Assignment Distillation](#label-assignment-distillation)
- [Balancing between Tasks](#balancing-between-tasks)
- [Miscellaneous Knowledge Distillation for General Object Detectors](#miscellaneous-knowledge-distillation-for-general-object-detectors)
- [Knowledge Distillation for Specific Object Detectors](#knowledge-distillation-for-specific-object-detectors)
- [Knowledge Distillation for GFL](#knowledge-distillation-for-gfl)
- [Knowledge Distillation for DETR](#knowledge-distillation-for-detr)
- [Knowledge Distillation for Heterogeneous Object Detectors](#knowledge-distillation-for-heterogeneous-object-detector-pairs)
- [Teacher Free Knowledge Distillation for Object Detectors](#teacher-free-knowledge-distillation-for-object-detectors)
- [Miscellaneous](#miscellaneous)
- [Newly Published](#newly-published)
- [Newly Published Papers](#newly-published-papers)

## Vanilla
## Knowledge Distillation for General Object Detectors

*NeurIPS 2017*.
\[[NeurIPS](https://proceedings.neurips.cc/paper/2017/hash/e1e32e235eee1f970470a3a6658dfdd5-Abstract.html)\]
Expand All @@ -34,100 +47,11 @@ Mimic. *CVPR 2017*.
- Mimicking Very Efficient Network for Object Detection
- Quanquan Li and Shengying Jin and Junjie Yan

TADF.
\[[arXiv](http://arxiv.org/abs/2006.13108)\]
\- A general distillation framework that adaptively transfers knowledge from teacher to student according to the task specific prior.

- Distilling Object Detectors with Task Adaptive Regularization
- Ruoyu Sun and Fuhui Tang and Xiaopeng Zhang and Hongkai Xiong and Qi Tian

GID. *CVPR 2021*.
\[[CVF](http://openaccess.thecvf.com/content/CVPR2021/html/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9577671/)\]
\[[arXiv](http://arxiv.org/abs/2103.02340)\]
\- A novel distillation method for detection tasks based on discriminative instances without considering the positive or negative distinguished by GT.

- General Instance Distillation for Object Detection
- Xing Dai and Zeren Jiang and Zhao Wu and Yiping Bao and Zhicheng Wang and Si Liu and Erjin Zhou

DSIG. *ICCV 2021*.
\[[CVF](http://openaccess.thecvf.com/content/ICCV2021/html/Chen_Deep_Structured_Instance_Graph_for_Distilling_Object_Detectors_ICCV_2021_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9711100/)\]
\[[arXiv](http://arxiv.org/abs/2109.12862)\]
<[GitHub](https://github.com/dvlab-research/Dsig)>
\- A simple knowledge structure to exploit and encode information inside the detection system to facilitate detector knowledge distillation.

- Deep Structured Instance Graph for Distilling Object Detectors
- Yixin Chen and Pengguang Chen and Shu Liu and Liwei Wang and Jiaya Jia

CD. *ICCV 2021*.
\[[CVF](http://openaccess.thecvf.com/content/ICCV2021/html/Shu_Channel-Wise_Knowledge_Distillation_for_Dense_Prediction_ICCV_2021_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9710762/)\]
\[[arXiv](http://arxiv.org/abs/2011.13256)\]
<[GitHub](https://github.com/irfanICMLL/TorchDistiller/tree/main/SemSeg-distill)>
\- Normalize the activation map of each channel to obtain a soft probability map.

- Channel-wise Knowledge Distillation for Dense Prediction
- Changyong Shu and Yifan Liu and Jianfei Gao and Zheng Yan and Chunhua Shen
### Feature Distillation

ICD. *NeurIPS 2021*.
\[[NeurIPS](https://proceedings.neurips.cc/paper_files/paper/2021/hash/892c91e0a653ba19df81a90f89d99bcd-Abstract.html)\]
\[[OpenReview](https://openreview.net/forum?id=k7aeAz4Vbb)\]
\[[arXiv](http://arxiv.org/abs/2110.12724)\]
<[GitHub](https://github.com/MegEngine/ICD)>
\- An instance-conditional distillation framework to find desired knowledge.
#### Foreground Masks

- Instance-Conditional Knowledge Distillation for Object Detection
- Zijian Kang and Peizhen Zhang and Xiangyu Zhang and Jian Sun and Nanning Zheng

LD. *CVPR 2022*.
\[[CVF](https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9878414/)\]
\[[arXiv](http://arxiv.org/abs/2102.12252)\]
<[GitHub](https://github.com/HikariTJU/LD)>
<[MMDet](https://github.com/open-mmlab/mmdetection/tree/master/configs/ld)>
\- Standard KD by adopting the general localization representation of bounding box.

- Localization Distillation for Dense Object Detection
- Zhaohui Zheng and Rongguang Ye and Ping Wang and Jun Wang and Dongwei Ren and Wangmeng Zuo

LAD. *WACV 2022*.
\[[CVF](https://openaccess.thecvf.com/content/WACV2022/html/Nguyen_Improving_Object_Detection_by_Label_Assignment_Distillation_WACV_2022_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9706993/)\]
\[[arXiv](http://arxiv.org/abs/2108.10520)\]
<[MMDet](https://github.com/open-mmlab/mmdetection/tree/master/configs/lad)>
\- Use the teacher network to generate labels for the student, through the hard labels dynamically assigned by the teacher.

- Improving Object Detection by Label Assignment Distillation
- Chuong H. Nguyen and Thuy C. Nguyen and Tuan N. Tang and Nam L. H. Phan

*AAAI 2022*.
\[[AAAI](https://ojs.aaai.org/index.php/AAAI/article/view/20018)\]
\[[arXiv](http://arxiv.org/abs/2112.04840)\]
\- RM takes the rank of candidate boxes from teachers as a new form of knowledge to distill. PFI attempts to correlate feature differences with prediction differences.

- Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-Guided Feature Imitation
- Gang Li and Xiang Li and Yujie Wang and Shanshan Zhang and Yichao Wu and Ding Liang

SSIM. *NeurIPS 2022*.
\[[OpenReview](https://openreview.net/forum?id=O3My0RK9s_R)\]
\[[arXiv](https://arxiv.org/abs/2211.13133v1)\]
<[GitHub](https://github.com/kornia/kornia)>
\- By taking into account additional contrast and structural cues, feature importance, correlation, and spatial dependence in the feature space are considered in the loss formulation.

- Structural Knowledge Distillation for Object Detection
- Philip De Rijk and Lukas Schneider and Marius Cordts and Dariu M Gavrila

DRKD. *IJCAI 2023*.
\[[arXiv](https://arxiv.org/abs/2302.05637)\]
\- Dual relation knowledge distillation, including pixel-wise relation distillation and instance-wise relation distillation

- Dual Relation Knowledge Distillation for Object Detection
- Zhenliang Ni and Fukui Yang and Shengzhao Wen and Gang Zhang

## Foreground Background

### Ground Truth Guided
##### Ground Truth Guided

FGFI. *CVPR 2019*.
\[[CVF](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Distilling_Object_Detectors_With_Fine-Grained_Feature_Imitation_CVPR_2019_paper.html)\]
Expand All @@ -148,7 +72,7 @@ DeFeat. *CVPR 2021*.
- Distilling Object Detectors via Decoupled Features
- Jianyuan Guo and Kai Han and Yunhe Wang and Han Wu and Xinghao Chen and Chunjing Xu and Chang Xu

### Prediction Guided
##### Prediction Guided

FRS. *NeurIPS 2021*.
\[[NeurIPS](https://proceedings.neurips.cc/paper_files/paper/2021/hash/29c0c0ee223856f336d7ea8052057753-Abstract.html)\]
Expand Down Expand Up @@ -177,7 +101,7 @@ TBD. *PR*.
- Task-balanced distillation for object detection
- Ruining Tang and Zhenyu Liu and Yangguang Li and Yiguo Song and Hui Liu and Qide Wang and Jing Shao and Guifang Duan and Jianrong Tan

### Attention Guided
##### Attention Guided

FKD. *ICLR 2021*.
\[[OpenReview](https://openreview.net/forum?id=uKhGRvM8QNH)\]
Expand Down Expand Up @@ -212,7 +136,155 @@ GLAMD. *ECCV 2022*.
- GLAMD: Global and Local Attention Mask Distillation for Object Detectors
- Younho Jang and Wheemyung Shin and Jinbeom Kim and Simon Woo and Sung-Ho Bae

## Heterogeneous
##### Miscellaneous Foreground Masks

CD. *ICCV 2021*.
\[[CVF](http://openaccess.thecvf.com/content/ICCV2021/html/Shu_Channel-Wise_Knowledge_Distillation_for_Dense_Prediction_ICCV_2021_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9710762/)\]
\[[arXiv](http://arxiv.org/abs/2011.13256)\]
<[GitHub](https://github.com/irfanICMLL/TorchDistiller/tree/main/SemSeg-distill)>
\- Normalize the activation map of each channel to obtain a soft probability map.

- Channel-wise Knowledge Distillation for Dense Prediction
- Changyong Shu and Yifan Liu and Jianfei Gao and Zheng Yan and Chunhua Shen

#### Miscellaneous Feature Distillation

DRKD. *IJCAI 2023*.
\[[arXiv](https://arxiv.org/abs/2302.05637)\]
\- Dual relation knowledge distillation, including pixel-wise relation distillation and instance-wise relation distillation

- Dual Relation Knowledge Distillation for Object Detection
- Zhenliang Ni and Fukui Yang and Shengzhao Wen and Gang Zhang

### Instance Distillation

GID. *CVPR 2021*.
\[[CVF](http://openaccess.thecvf.com/content/CVPR2021/html/Dai_General_Instance_Distillation_for_Object_Detection_CVPR_2021_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9577671/)\]
\[[arXiv](http://arxiv.org/abs/2103.02340)\]
\- A novel distillation method for detection tasks based on discriminative instances without considering the positive or negative distinguished by GT.

- General Instance Distillation for Object Detection
- Xing Dai and Zeren Jiang and Zhao Wu and Yiping Bao and Zhicheng Wang and Si Liu and Erjin Zhou

DSIG. *ICCV 2021*.
\[[CVF](http://openaccess.thecvf.com/content/ICCV2021/html/Chen_Deep_Structured_Instance_Graph_for_Distilling_Object_Detectors_ICCV_2021_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9711100/)\]
\[[arXiv](http://arxiv.org/abs/2109.12862)\]
<[GitHub](https://github.com/dvlab-research/Dsig)>
\- A simple knowledge structure to exploit and encode information inside the detection system to facilitate detector knowledge distillation.

- Deep Structured Instance Graph for Distilling Object Detectors
- Yixin Chen and Pengguang Chen and Shu Liu and Liwei Wang and Jiaya Jia

ICD. *NeurIPS 2021*.
\[[NeurIPS](https://proceedings.neurips.cc/paper_files/paper/2021/hash/892c91e0a653ba19df81a90f89d99bcd-Abstract.html)\]
\[[OpenReview](https://openreview.net/forum?id=k7aeAz4Vbb)\]
\[[arXiv](http://arxiv.org/abs/2110.12724)\]
<[GitHub](https://github.com/MegEngine/ICD)>
\- An instance-conditional distillation framework to find desired knowledge.

- Instance-Conditional Knowledge Distillation for Object Detection
- Zijian Kang and Peizhen Zhang and Xiangyu Zhang and Jian Sun and Nanning Zheng

### Label Assignment Distillation

LAD. *WACV 2022*.
\[[CVF](https://openaccess.thecvf.com/content/WACV2022/html/Nguyen_Improving_Object_Detection_by_Label_Assignment_Distillation_WACV_2022_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9706993/)\]
\[[arXiv](http://arxiv.org/abs/2108.10520)\]
<[MMDet](https://github.com/open-mmlab/mmdetection/tree/master/configs/lad)>
\- Use the teacher network to generate labels for the student, through the hard labels dynamically assigned by the teacher.

- Improving Object Detection by Label Assignment Distillation
- Chuong H. Nguyen and Thuy C. Nguyen and Tuan N. Tang and Nam L. H. Phan

### Balancing between Tasks

TADF.
\[[arXiv](http://arxiv.org/abs/2006.13108)\]
\- A general distillation framework that adaptively transfers knowledge from teacher to student according to the task specific prior.

- Distilling Object Detectors with Task Adaptive Regularization
- Ruoyu Sun and Fuhui Tang and Xiaopeng Zhang and Hongkai Xiong and Qi Tian

BCKD. *ICCV 2023*
\[[CVF](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Bridging_Cross-task_Protocol_Inconsistency_for_Distillation_in_Dense_Object_Detection_ICCV_2023_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/10377607)\]
\[[arXiv](http://arxiv.org/abs/2308.14286)\]
\- A novel distillation method with cross-task consistent protocols, tailored for the dense object detection.

- Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
- Longrong Yang and Xianpan Zhou and Xuewei Li and Liang Qiao and Zheyang Li and Ziwei Yang and Gaoang Wang and Xi Li

### Miscellaneous Knowledge Distillation for General Object Detectors

*AAAI 2022*.
\[[AAAI](https://ojs.aaai.org/index.php/AAAI/article/view/20018)\]
\[[arXiv](http://arxiv.org/abs/2112.04840)\]
\- RM takes the rank of candidate boxes from teachers as a new form of knowledge to distill. PFI attempts to correlate feature differences with prediction differences.

- Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-Guided Feature Imitation
- Gang Li and Xiang Li and Yujie Wang and Shanshan Zhang and Yichao Wu and Ding Liang

*NeurIPS 2022*.
\[[OpenReview](https://openreview.net/forum?id=O3My0RK9s_R)\]
\[[arXiv](https://arxiv.org/abs/2211.13133v1)\]
<[GitHub](https://github.com/kornia/kornia)>
\- By taking into account additional contrast and structural cues, feature importance, correlation, and spatial dependence in the feature space are considered in the loss formulation.

- Structural Knowledge Distillation for Object Detection
- Philip De Rijk and Lukas Schneider and Marius Cordts and Dariu M Gavrila

CrossKD.
\[[arXiv](https://arxiv.org/abs/2306.11369)\]
<[GitHub](https://github.com/jbwang1997/CrossKD)>
\- Delivers the intermediate features of the student's detection head to the teacher's detection head

- CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection
- Jiabao Wang and Yuming Chen and Zhaohui Zheng and Xiang Li and Ming-Ming Cheng and Qibin Hou

## Knowledge Distillation for Specific Object Detectors

### Knowledge Distillation for GFL

LD. *CVPR 2022*.
\[[CVF](https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/9878414/)\]
\[[arXiv](http://arxiv.org/abs/2102.12252)\]
<[GitHub](https://github.com/HikariTJU/LD)>
<[MMDet](https://github.com/open-mmlab/mmdetection/tree/master/configs/ld)>
\- Standard KD by adopting the general localization representation of bounding box.

- Localization Distillation for Dense Object Detection
- Zhaohui Zheng and Rongguang Ye and Ping Wang and Jun Wang and Dongwei Ren and Wangmeng Zuo

### Knowledge Distillation for DETR

DETRDistill. *ICCV 2023*.
\[[CVF](https://openaccess.thecvf.com/content/ICCV2023/html/Chang_DETRDistill_A_Universal_Knowledge_Distillation_Framework_for_DETR-families_ICCV_2023_paper.html)\]
\[[arXiv](http://arxiv.org/abs/2211.10156)\]
\- A novel knowledge distillation dedicated to DETR-families.

- DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
- Jiahao Chang and Shuo Wang and Guangkai Xu and Zehui Chen and Chenhongyi Yang and Feng Zhao

D^3^ETR.
\[[arXiv](http://arxiv.org/abs/2211.09768)\]
\- Distills knowledge in decoder predictions and attention maps from the teachers to students.

- D^3^ETR: Decoder Distillation for Detection Transformer
- Xiaokang Chen and Jiahui Chen and Yan Liu and Gang Zeng

KD-DETR.
\[[arXiv](http://arxiv.org/abs/2211.08071)\]
\- A general knowledge distillation paradigm for DETR with consistent distillation points sampling.

- Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
- Yu Wang and Xin Li and Shengzhao Wen and Fukui Yang and Wanping Zhang and Gang Zhang and Haocheng Feng and Junyu Han and Errui Ding

## Knowledge Distillation for Heterogeneous Object Detector Pairs

G-DetKD. *ICCV 2021*.
\[[CVF](http://openaccess.thecvf.com/content/ICCV2021/html/Yao_G-DetKD_Towards_General_Distillation_Framework_for_Object_Detectors_via_Contrastive_ICCV_2021_paper.html)\]
Expand Down Expand Up @@ -242,7 +314,7 @@ PKD. *NeurIPS 2022*.
- PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
- Weihan Cao and Yifan Zhang and Jianfei Gao and Anda Cheng and Ke Cheng and Jian Cheng

## Teacher Free
## Teacher Free Knowledge Distillation for Object Detectors

MimicDet. *ECCV 2020*.
\[[ECVA](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590528.pdf)\]
Expand Down Expand Up @@ -281,6 +353,15 @@ LGD. *AAAI 2022*.
- LGD: Label-Guided Self-Distillation for Object Detection
- Peizhen Zhang and Zijian Kang and Tong Yang and Xiangyu Zhang and Nanning Zheng and Jian Sun

SSD-Det. *ICCV 2023*.
\[[CVF](https://openaccess.thecvf.com/content/ICCV2023/html/Wu_Spatial_Self-Distillation_for_Object_Detection_with_Inaccurate_Bounding_Boxes_ICCV_2023_paper.html)\]
\[[IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/10377611)\]
\[[arXiv](http://arxiv.org/abs/2307.12101)\]
\- Mine spatial information to refine the inaccurate box in a self-distillation fashion.

- Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
- Di Wu and Pengfei Chen and Xuehui Yu and Guorong Li and Zhenjun Han and Jianbin Jiao

## Miscellaneous

*TPAMI*.
Expand All @@ -297,12 +378,4 @@ ScaleKD. *CVPR 2023*.
- ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector
- Yichen Zhu and Qiqi Zhou and Ning Liu and Zhiyuan Xu and Zhicai Ou and Xiaofeng Mou and Jian Tang

## Newly Published

CrossKD.
\[[arXiv](https://arxiv.org/abs/2306.11369)\]
<[GitHub](https://github.com/jbwang1997/CrossKD)>
\- Delivers the intermediate features of the student's detection head to the teacher's detection head

- CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection
- Jiabao Wang and Yuming Chen and Zhaohui Zheng and Xiang Li and Ming-Ming Cheng and Qibin Hou
## Newly Published Papers

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