Fetching the paper…
Reading the bibliography…
For years, the YOLO series has been the de facto industry-level standard for efficient object detection.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, Andrew Y Ng, et al · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Unitbox: An advanced object detection network
Jiahui Yu, Yuning Jiang, Zhangyang Wang, Zhimin Cao, and Thomas Huang · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Earlier work this paper cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Earlier work this paper cites.
Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Earlier work this paper cites.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
Earlier work this paper cites.
Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
Cited alongside, same era.
Path aggregation network for instance segmentation
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia · 2018
Cited alongside, same era.
TensorRT
NVIDIA · 2018
Cited alongside, same era.
Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
Cited alongside, same era.
Nas-fpn: Learning scalable feature pyramid architecture for object detection
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2019
Cited alongside, same era.
Mish: A self regularized non-monotonic neural activation function
Diganta Misra · 2019
Ota: Optimal transport assignment for object detection
Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, and Jian Sun · 2021
Later among the works it cites.
Yolox: Exceeding yolo series in 2021
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun · 2021
Later among the works it cites.
α \alpha -iou: A family of power intersection over union losses for bounding box regression
Jiabo He, Sarah Erfani, Xingjun Ma, James Bailey, Ying Chi, and Xian-Sheng Hua · 2021
Later among the works it cites.
Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection
Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang · 2021
Later among the works it cites.
pytorch-quantization’s documentation
NVIDIA · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
Cited alongside, same era.
FCOS: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
Cited alongside, same era.
Reppoints: Point set representation for object detection
Ze Yang, Shaohui Liu, Han Hu, Liwei Wang, and Stephen Lin · 2019
Cited alongside, same era.
Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
Cited alongside, same era.
Yolov4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Changyong Shu, Yifan Liu, Jianfei Gao, Zheng Yan, and Chunhua Shen · 2021
Later among the works it cites.
Varifocalnet: An iou-aware dense object detector
Haoyang Zhang, Ying Wang, Feras Dayoub, and Niko Sunderhauf · 2021
Later among the works it cites.
Re-parameterizing your optimizers rather than architectures
Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Kaiqi Huang, Jungong Han, and Guiguang Ding · 2022
Closest in time.
Siou loss: More powerful learning for bounding box regression
Zhora Gevorgyan · 2022
Closest in time.
YOLOv5 release v6.1
Jocher Glenn · 2022
Closest in time.
Polyloss: A polynomial expansion perspective of classification loss functions
Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, and Dragomir Anguelov · 2022
Closest in time.
A dual weighting label assignment scheme for object detection
Shuai Li, Chenhang He, Ruihuang Li, and Lei Zhang · 2022
Closest in time.
PaddleSlim documentation
PaddleSlim · 2022
Closest in time.
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 · 2022
Closest in time.
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
Closest in time.
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
Closest in time.
Objectbox: From centers to boxes for anchor-free object detection
Mohsen Zand, Ali Etemad, and Michael A. Greenspan · 2022
Closest in time.