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Detection Papers

Review materials

[综述：目标检测二十年（2001-2021）]
[Ranking list on COCO test-dev]
[Anchor free detectors]
[detection-transformer-list]

Transformer-based Object Detection

  • MIMDET Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection. arXiv 2022-04
    Yuxin Fang, Shusheng Yang, Shijie Wang, Yixiao Ge, Ying Shan, Xinggang Wang
    [paper] [code]

    Notes MIMDET
    • Key points:
      • With randomly sampled partial observations (25%-50%), the ViT model pretrained using MIM works well for object detection.
      • Using the features of both ViT and CNNs to generate a feature pyramid for detection.
    • Performance:
      • 51.5 AP on COCO val2017

  • DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. arXiv 2022-03
    Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum
    [paper] [code]

    Notes DINO
    • Key points:
      • A contrastive way for denoising training, a mixed query selection method for anchor initialization, and a forward twice scheme for box prediction.
    • Performance:
      • 63.2 AP on COCO val2017 with a SiwnL backbone
      • 63.3 AP on test-dev

  • Exploring Plain Vision Transformer Backbones for Object Detection. arXiv 2022-03
    Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He
    [paper] [code]

    Notes referformer
    • Key points:
      • Exploring a plain, non-hierarchical vision transformer as the backbone network for object detection.
      • Using window attention aided with very few cross-window propagation blocks is sufficient for information interaction.
    • Performance:
      • 60.4 AP(box) on COCO, with ViT-H backbone (MAE pretraining on 1K) and Cascade framework.

  • DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR. ICLR 2022
    Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang
    [paper] [code]

  • DN-DETR: Accelerate DETR Training by Introducing Query DeNoising. CVPR 2022
    Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, Lei Zhang
    [paper] [code] [related materials]

  • Accelerating DETR Convergence via Semantic-Aligned Matching. CVPR 2022
    Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Kaiwen Cui, Shijian Lu
    [paper] [code] [related materials]

  • AdaMixer: A Fast-Converging Query-Based Object Detector. CVPR 2022
    Ziteng Gao, Limin Wang, Bing Han, Sheng Guo
    [paper] [code]

    Notes adaMixer

    • Key points:

      • Improving the adaptability of the decoder processes in a) each query sample features over space and scales based on estimated offsets; b) decoding these sampled features with an adaptive MLP-mixer under the guidance of each query.

    • Performance:

      • 51.3 AP(box) on COCO minival.

  • Few-Shot Object Detection with Fully Cross-Transformer. CVPR 2022
    Guangxing Han, Jiawei Ma, Shiyuan Huang, Long Chen, Shih-Fu Chang
    [paper]

  • Multi-Granularity Alignment Domain Adaptation for Object Detection. CVPR 2022
    Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu
    [paper] [code]

  • UP-DETR: Unsupervised Pre-training for Object Detection with Transformers. CVPR 2021
    Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen. [paper] [code]

  • Deformable DETR: Deformable Transformers for End-to-End Object Detection. CVPR 2021
    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
    [paper] [code]

  • Adaptive Image Transformer for One-Shot Object Detection. CVPR 2021

  • DETR - End-to-end object detection with transformers. ECCV2020
    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko. [paper] [code]

    Notes DETR

    • Key points:

      • Casting object detection as a set prediction problem and solving it with a set-based global loss that forces unique predictions via bipartite matching under a transformer encoder-decoder architecture.
      • Given a small set of learned object queries, DETR reasons about the relations of the objects and the global image context to output the prediction set in parallel.

    • Performance:

      • 45.1 AP(box) on COCO 2017val, with the res101 backbone.

Semi-Supervised, UN-supervised, and self-supervised Object Detection

ArXiv 2022

  • Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach
    Sai Saketh Rambhatla, Saksham Suri, Rama Chellappa, Abhinav Shrivastava
    [paper]

CVPR 2022

  • Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection
    Li Yin, Juan M Perez-Rua, Kevin J Liang
    [paper]

ArXiv 2021

  • CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
    Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister
    [paper]

  • End-to-End Semi-Supervised Object Detection with Soft Teacher
    Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, Zicheng Liu
    [paper] [code]

CVPR 2021

  • Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
    Qiang Zhou, Chaohui Yu, Zhibin Wang, Qi Qian, Hao Li
    [paper]

  • Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
    Zhenyu Wang, Yali Li, Ye Guo, Lu Fang, Shengjin Wang
    [paper]

  • Interactive Self-Training With Mean Teachers for Semi-Supervised Object Detection Qize Yang, Xihan Wei, Biao Wang, Xian-Sheng Hua, Lei Zhang [paper]

  • Points as Queries: Weakly Semi-supervised Object Detection by Points Liangyu Chen, Tong Yang, Xiangyu Zhang, Wei Zhang, Jian Sun [paper]

  • Interpolation-based semi-supervised learning for object detection Jisoo Jeong, Vikas Verma, Minsung Hyun, Juho Kannala, Nojun Kwak [paper] [code]

  • DAP: Detection-Aware Pre-training with Weak Supervision Yuanyi Zhong, Jianfeng Wang, Lijuan Wang, Jian Peng, Yu-Xiong Wang, Lei Zhang [paper]

  • Instance Localization for Self-supervised Detection Pretraining Ceyuan Yang, Zhirong Wu, Bolei Zhou, Stephen Lin [paper] [code]

  • Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms

  • Humble Teachers Teach Better Students for Semi-Supervised Object Detection
    Yihe Tang, Weifeng Chen, Yijun Luo, Yuting Zhang
    [paper] [code]

3D Object Detection

  • CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022 Yanan Zhang, Jiaxin Chen, Di Huang
    [paper] [code]

Dense Object Detection

CVPR 2022

  • Localization Distillation for Dense Object Detection
    Zhaohui Zheng, Rongguang Ye, Ping Wang, Dongwei Ren, Wangmeng Zuo, Qibin Hou, Ming-Ming Cheng
    [paper] [code]

CVPR 2021

  • Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang [paper] [code]

  • VarifocalNet: An IoU-aware Dense Object Detector Haoyang Zhang, Ying Wang, Feras Dayoub, Niko Sünderhauf [paper] [code]

Domain Adaption

CVPR 2021

  • Instance-Invariant Domain Adaptive Object Detection via Progressive Disentanglement Aming Wu, Yahong Han, Linchao Zhu, Yi Yang [paper]

  • Domain-Specific Suppression for Adaptive Object Detection Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, YangYang Xia, XiShan Zhang, ShaoLi Liu [paper]

  • Unbiased Mean Teacher for Cross Domain Object Detection Jinhong Deng, Wen Li, Yuhua Chen, Lixin Duan [paper] [code]

  • MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A. Sindagi, Vishal M. Patel [paper]

New Framework

CVPR 2021

  • Object Detection as a Positive-Unlabeled Problem Yuewei Yang, Kevin J Liang, Lawrence Carin [paper] [code]

  • DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution Siyuan Qiao, Liang-Chieh Chen, Alan Yuille [paper] [code]

  • I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu [paper] [code]

  • Sparse R-CNN: End-to-End Object Detection with Learnable Proposals Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, Ping Luo [paper] [code]

  • End-to-End Object Detection with Fully Convolutional Network Jianfeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng [paper] [code]

  • You Only Look One-level Feature Qiang Chen, Yingming Wang, Tong Yang, Xiangyu Zhang, Jian Cheng, Jian Sun [paper] [code]

  • AQD: Towards Accurate Quantized Object Detection. Peng Chen, Jing Liu, Bohan Zhuang, Mingkui Tan, Chunhua Shen. [paper] [code]

Few-Shot

CVPR 2021

  • Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection Hanzhe Hu, Shuai Bai, Aoxue Li, Jinshi Cui, Liwei Wang [paper] [code]

  • Hallucination Improves Few-Shot Object Detection Weilin Zhang, Yu-Xiong Wang [paper] [code]

  • Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, Marios Savvides [paper]

  • FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, Chi Zhang [paper] [code]

  • Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye [paper] [code]

  • Generalized Few-Shot Object Detection without Forgetting Zhibo Fan, Yuchen Ma, Zeming Li, Jian Sun [paper]

  • Few-Shot Object Detection via Classification Refinement and Distractor Retreatment

  • Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss

  • Transformation Invariant Few-Shot Object Detection

Lightweight (distillation and NAS)

CVPR 2021

  • MobileDets: Searching for Object Detection Architectures for Mobile Accelerators Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen [paper] [code]

  • General Instance Distillation for Object Detection Xing Dai, Zeren Jiang, Zhao Wu, Yiping Bao, Zhicheng Wang, Si Liu, Erjin Zhou [paper]

  • Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang
    [paper]

  • OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection Tingting Liang, Yongtao Wang, Zhi Tang, Guosheng Hu, Haibin Ling [paper] [code]

  • Distilling Object Detectors via Decoupled Features Jianyuan Guo, Kai Han, Yunhe Wang, Han Wu, Xinghao Chen, Chunjing Xu, Chang Xu [paper] [code]

Long-tail

CVPR 2021

  • Adaptive Class Suppression Loss for Long-Tail Object Detection Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Jinqiao Wang, Ming Tang [paper] [code]

  • Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, Quanquan Li [paper] [code]

training method

CVPR 2021

  • IQDet: Instance-wise Quality Distribution Sampling for Object Detection
    Yuchen Ma, Songtao Liu, Zeming Li, Jian Sun
    [paper]

  • Multiple instance active learning for object detection
    Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, Qixiang Ye
    [paper] [code]

  • Scale-aware Automatic Augmentation for Object Detection
    Yukang Chen, Yanwei Li, Tao Kong, Lu Qi, Ruihang Chu, Lei Li, Jiaya Jia
    [paper] [code]

  • Class-Aware Robust Adversarial Training for Object Detection
    Pin-Chun Chen, Bo-Han Kung, Jun-Cheng Chen
    [paper]

  • Robust and Accurate Object Detection via Adversarial Learning
    Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong
    [paper] [code]

Specific object\problem detection

CVPR 2021

  • CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement.
    Noranart Vesdapunt, Baoyuan Wang.
    [paper]

  • Improved Handling of Motion Blur in Online Object Detection
    Mohamed Sayed, Gabriel Brostow
    [paper]

  • PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
    Tal Reiss, Niv Cohen, Liron Bergman, Yedid Hoshen
    [paper] [code]

  • Towards Open World Object Detection
    K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian
    [paper] [code]

  • UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation
    Siddhesh Khandelwal, Raghav Goyal, Leonid Sigal
    [paper]

  • Dogfight: Detecting Drones from Drones Videos
    Muhammad Waseem Ashraf, Waqas Sultani, Mubarak Shah
    [paper]

  • Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
    Longyin Wen, Dawei Du, Pengfei Zhu, Qinghua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu
    [paper] [code]

  • Generalizable Pedestrian Detection: The Elephant In The Room
    Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram, Ling Shao
    [paper] [code]

  • Black-box Explanation of Object Detectors via Saliency Maps
    Vitali Petsiuk, Rajiv Jain, Varun Manjunatha, Vlad I. Morariu, Ashutosh Mehra, Vicente Ordonez, Kate Saenko
    [paper]

  • Variational Pedestrian Detection
    Yuang Zhang, Huanyu He, Jianguo Li, Yuxi Li, John See, Weiyao Lin
    [paper]

  • MOOD: Multi-level Out-of-distribution Detection
    Ziqian Lin, Sreya Dutta Roy, Yixuan Li
    [paper] [code]

  • A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection
    Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Ngai-Man Cheung
    [paper] [code]

  • OTA: Optimal Transport Assignment for Object Detection
    Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, Jian Sun
    [paper] [code]

Localization

  • Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization. CVPR 2022
    Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, Sungroh Yoon
    [paper] [code]

Unclassified

arXiv 2022

  • BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
    Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue
    [paper] [code]

  • PP-YOLOE: An evolved version of YOLO
    Shangliang Xu, Xinxin Wang, Wenyu Lv, Qinyao Chang, Cheng Cui, Kaipeng Deng, Guanzhong Wang, Qingqing Dang, Shengyu Wei, Yuning Du, Baohua Lai
    [paper] [code]

CVPR 2022

  • Proper Reuse of Image Classification Features Improves Object Detection
    Cristina Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin
    [paper] [code]

  • Expanding Low-Density Latent Regions for Open-Set Object Detection
    Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia
    [paper]

  • Optimal Correction Cost for Object Detection Evaluation
    Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh
    [paper]

ArXiv 2021

  • 2nd Place Solution for Waymo Open Dataset Challenge — Real-time 2D Object Detection Yueming Zhang, Xiaolin Song, Bing Bai, Tengfei Xing, Chao Liu, Xin Gao, Zhihui Wang, Yawei Wen, Haojin Liao, Guoshan Zhang, Pengfei Xu
    [paper] [code]

  • Towards Total Recall in Industrial Anomaly Detection
    [paper]

  • MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
    Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun
    [paper] [code] [ruanwen]

  • YOLOX: Exceeding YOLO Series in 2021
    Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun
    [paper] [code]

  • CBNetV2: A Composite Backbone Network Architecture for Object Detection
    Tingting Liang, Xiaojie Chu, Yudong Liu, Yongtao Wang, Zhi Tang, Wei Chu, Jingdong Chen, Haibin Ling
    [paper] [code]

CVPR 2021

  • RPN Prototype Alignment for Domain Adaptive Object Detector

  • Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

  • Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection

  • Dynamic Head: Unifying Object Detection Heads With Attentions
    Xiyang Dai, Yinpeng Chen, Bin Xiao, Dongdong Chen, Mengchen Liu, Lu Yuan, Lei Zhang
    [paper] [code]

  • Layer-Wise Searching for 1-Bit Detectors

  • Positive-Unlabeled Data Purification in the Wild for Object Detection

  • GAIA: A Transfer Learning System of Object Detection That Fits Your Needs.

  • RankDetNet: Delving Into Ranking Constraints for Object Detection.

Related fields

  • Generalized Domain Adaptation
    Yu Mitsuzumi Go Irie Daiki Ikami Takashi Shibata
    [paper] [code]