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Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models.
Mmdetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al · 1906
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A comparative analysis of selection schemes used in genetic algorithms
David E Goldberg and Kalyanmoy Deb · 1991
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Evolving normalization-activation layers
Hanxiao Liu, Andrew Brock, Karen Simonyan, and Quoc V Le · 2004
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DEAP: Evolutionary algorithms made easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné · 2012
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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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The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
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Fast r-cnn
Ross Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unitbox: An advanced object detection network
Jiahui Yu, Yuning Jiang, Zhangyang Wang, Zhimin Cao, and Thomas Huang · 2016
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
Cited alongside, same era.
Acquisition of localization confidence for accurate object detection
Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, and Yuning Jiang · 2018
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2018
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Improving object localization with fitness nms and bounded iou loss
Lachlan Tychsen-Smith and Lars Petersson · 2018
Cited alongside, same era.
Improved training speed, accuracy, and data utilization through loss function optimization
Santiago Gonzalez and Risto Miikkulainen · 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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Dr loss: Improving object detection by distributional ranking
Qi Qian, Lei Chen, Hao Li, and Rong Jin · 2020
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Automl-zero: Evolving machine learning algorithms from scratch
Esteban Real, Chen Liang, David R So, and Quoc V Le · 2020
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Loss function search for face recognition
Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, and Tao Mei · 2020
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Iou-aware single-stage object detector for accurate localization
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Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2019
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.
Towards accurate one-stage object detection with ap-loss
Kean Chen, Jianguo Li, Weiyao Lin, John See, Ji Wang, Lingyu Duan, Zhibo Chen, Changwei He, and Junni Zou
Cited in the paper.
Gradient harmonized single-stage detector
Buyu Li, Yu Liu, and Xiaogang Wang
Cited in the paper.
Am-lfs: Automl for loss function search
Chuming Li, Xin Yuan, Chen Lin, Minghao Guo, Wei Wu, Junjie Yan, and Wanli Ouyang
Cited in the paper.
Shengkai Wu, Xiaoping Li, and Xinggang Wang · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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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
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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
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