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Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Fast r-cnn
Ross Girshick · 2015
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Deeply-supervised nets
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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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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Mask r-cnn
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 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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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Mmdetection: Open mmlab detection toolbox and benchmark
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Class-balanced loss based on effective number of samples
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Lvis: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollar, and Ross Girshick · 2019
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2019
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Rethinking the value of labels for improving class-imbalanced learning
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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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Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2020
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Image-level or object-level? a tale of two resampling strategies for long-tailed detection
Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, and Jose M Alvarez · 2021
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Exploring classification equilibrium in long-tailed object detection
Chengjian Feng, Yujie Zhong, and Weilin Huang · 2021
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Fcos: Fully convolutional one-stage object detection
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Detection in crowded scenes: One proposal, multiple predictions
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Elf: An early-exiting framework for long-tailed classification
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Overcoming classifier imbalance for long-tail object detection with balanced group softmax
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Balanced meta-softmax for long-tailed visual recognition
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Equalization loss for long-tailed object recognition
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Long-tailed classification by keeping the good and removing the bad momentum causal effect
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Distilling virtual examples for long-tailed recognition
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Disentangling label distribution for long-tailed visual recognition
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Droploss for long-tail instance segmentation
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Self supervision to distillation for long-tailed visual recognition
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On model calibration for long-tailed object detection and instance segmentation
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Equalization loss v2: A new gradient balance approach for long-tailed object detection
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Seesaw loss for long-tailed instance segmentation
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Adaptive class suppression loss for long-tail object detection
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Mosaicos: a simple and effective use of object-centric images for long-tailed object detection
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Delving deep into label smoothing
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Distribution alignment: A unified framework for long-tail visual recognition
Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, and Jian Sun · 2021
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Bag of tricks for long-tailed visual recognition with deep convolutional neural networks
Yongshun Zhang, Xiu-Shen Wei, Boyan Zhou, and Jianxin Wu · 2021
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