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Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Fast r-cnn
Ross Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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Darknet: Open source neural networks in c
Joseph Redmon · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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End-to-end people detection in crowded scenes
Russell Stewart, Mykhaylo Andriluka, and Andrew Y Ng · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Learning non-maximum suppression
Jan Hosang, Rodrigo Benenson, and Bernt Schiele · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Yolo9000: Better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Relation networks for object detection
Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, and Yichen Wei · 2018
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Path aggregation network for instance segmentation
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia · 2018
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Yolov3: An incremental improvement, 2018
Joseph Redmon and Ali Farhadi · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Mobile robot navigation using an object recognition software with rgbd images and the yolo algorithm
Douglas Henke Dos Reis, Daniel Welfer, Marco Antonio De Souza Leite Cuadros, and Daniel Fernando Tello Gamarra · 2019
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Centernet: Keypoint triplets for object detection
Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian · 2019
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Statistical aspects of wasserstein distances
Victor M Panaretos and Yoav Zemel · 2019
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Objects365: A large-scale, high-quality dataset for object detection
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun · 2019
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Yolov4: Optimal speed and accuracy of object detection, 2020
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang · 2020
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Neural architecture design for gpu-efficient networks
Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin · 2020
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Fcos: A simple and strong anchor-free object detector
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2020
Cited alongside, same era.
Cspnet: A new backbone that can enhance learning capability of cnn
Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh · 2020
Cited alongside, same era.
Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection
Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, and Stan Z Li · 2020
Cited alongside, same era.
Distance-iou loss: Faster and better learning for bounding box regression
Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren · 2020
Cited alongside, same era.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Later among the works it cites.
Rtmdet: An empirical study of designing real-time object detectors
Chengqi Lyu, Wenwei Zhang, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang, and Kai Chen · 2022
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Designing network design strategies through gradient path analysis
Chien-Yao Wang, Hong-Yuan Mark Liao, and I-Hau Yeh · 2022
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Anchor detr: Query design for transformer-based detector
Yingming Wang, Xiangyu Zhang, Tong Yang, and Jian Sun · 2022
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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, et al · 2022
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Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
Cited alongside, same era.
Repvgg: Making vgg-style convnets great again
Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
Cited alongside, same era.
Tood: Task-aligned one-stage object detection
Chengjian Feng, Yujie Zhong, Yu Gao, Matthew R Scott, and Weilin Huang · 2021
Cited alongside, same era.
Yolox: Exceeding yolo series in 2021
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun · 2021
Cited alongside, same era.
Simple copy-paste is a strong data augmentation method for instance segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
Cited alongside, same era.
Levit: a vision transformer in convnet’s clothing for faster inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze · 2021
Cited alongside, same era.
Later among the works it cites.
Damo-yolo: A report on real-time object detection design
Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang, and Xiuyu Sun · 2022
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Motr: End-to-end multiple-object tracking with transformer
Fangao Zeng, Bin Dong, Yuang Zhang, Tiancai Wang, Xiangyu Zhang, and Yichen Wei · 2022
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Dino: Detr with improved denoising anchor boxes for end-to-end object detection
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum · 2022
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Topformer: Token pyramid transformer for mobile semantic segmentation
Wenqiang Zhang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu, and Chunhua Shen · 2022
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Group detr: Fast detr training with group-wise one-to-many assignment
Qiang Chen, Xiaokang Chen, Jian Wang, Shan Zhang, Kun Yao, Haocheng Feng, Junyu Han, Errui Ding, Gang Zeng, and Jingdong Wang · 2023
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Enhancing your trained detrs with box refinement
Yiqun Chen, Qiang Chen, Peize Sun, Shoufa Chen, Jingdong Wang, and Jian Cheng · 2023
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Yolo-ms: rethinking multi-scale representation learning for real-time object detection
Yuming Chen, Xinbin Yuan, Ruiqi Wu, Jiabao Wang, Qibin Hou, and Ming-Ming Cheng · 2023
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Detrs with hybrid matching
Ding Jia, Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, Weihong Lin, Lei Sun, Chao Zhang, and Han Hu · 2023
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Yolov6 v3.0: A full-scale reloading
Chuyi Li, Lulu Li, Yifei Geng, Hongliang Jiang, Meng Cheng, Bo Zhang, Zaidan Ke, Xiaoming Xu, and Xiangxiang Chu · 2023
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Deyov2: Rank feature with greedy matching for end-to-end object detection
Haodong Ouyang · 2023
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Deyov3: Detr with yolo for real-time object detection
Haodong Ouyang · 2023
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Repvit: Revisiting mobile cnn from vit perspective
Ao Wang, Hui Chen, Zijia Lin, Hengjun Pu, and Guiguang Ding · 2023
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Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao · 2023
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Vision transformer with attention map hallucination and ffn compaction
Haiyang Xu, Zhichao Zhou, Dongliang He, Fu Li, and Jingdong Wang · 2023
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Detrs beat yolos on real-time object detection
Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, and Jie Chen · 2023
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Object detection made simpler by eliminating heuristic nms
Qiang Zhou and Chaohui Yu · 2023
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Detrs with collaborative hybrid assignments training
Zhuofan Zong, Guanglu Song, and Yu Liu · 2023
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Deyo: Detr with yolo for end-to-end object detection
Haodong Ouyang · 2024
Closest in time.
Lranet: Towards accurate and efficient scene text detection with low-rank approximation network
Yuchen Su, Zhineng Chen, Zhiwen Shao, Yuning Du, Zhilong Ji, Jinfeng Bai, Yong Zhou, and Yu-Gang Jiang · 2024
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Gold-yolo: Efficient object detector via gather-and-distribute mechanism
Chengcheng Wang, Wei He, Ying Nie, Jianyuan Guo, Chuanjian Liu, Yunhe Wang, and Kai Han · 2024
Closest in time.
Yolov9: Learning what you want to learn using programmable gradient information
Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao · 2024
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Ms-detr: Efficient detr training with mixed supervision
Chuyang Zhao, Yifan Sun, Wenhao Wang, Qiang Chen, Errui Ding, Yi Yang, and Jingdong Wang · 2024
Closest in time.