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Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Simultaneous detection and segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2014
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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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Fast r-cnn
Ross Girshick · 2015
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Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 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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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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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 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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Instance-aware semantic segmentation via multi-task network cascades
Jifeng Dai, Kaiming He, and Jian Sun · 2016
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Multiview rgb-d dataset for object instance detection
Georgios Georgakis, Md Alimoor Reza, Arsalan Mousavian, Phi-Hung Le, and Jana Košecká · 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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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Debidatta Dwibedi, Ishan Misra, and Martial Hebert · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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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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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge Belongie · 2018
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Modeling visual context is key to augmenting object detection datasets
Nikita Dvornik, Julien Mairal, and Cordelia Schmid · 2018
Cited alongside, same era.
Detectron, 2018
Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollár, and Kaiming He · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
Cited alongside, same era.
Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 2018
Cited alongside, same era.
Learning to segment via cut-and-paste
Tal Remez, Jonathan Huang, and Matthew Brown · 2018
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Sniper: Efficient multi-scale training
Bharat Singh, Mahyar Najibi, and Larry S Davis · 2018
Cited alongside, same era.
Bag of freebies for training object detection neural networks
Zhi Zhang, Tong He, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2019
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Yolov4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Spinenet: Learning scale-permuted backbone for recognition and localization
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V Le, and Xiaodan Song · 2020
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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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Single-shot refinement neural network for object detection
Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, and Stan Z Li · 2018
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Exfuse: Enhancing feature fusion for semantic segmentation
Zhenli Zhang, Xiangyu Zhang, Chao Peng, Xiangyang Xue, and Jian Sun · 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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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Learning to segment the tail
Xinting Hu, Yi Jiang, Kaihua Tang, Jingyuan Chen, Chunyan Miao, and Hanwang Zhang · 2020
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Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective
Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, and Boqing Gong · 2020
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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 · 2020
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Overcoming classifier imbalance for long-tail object detection with balanced group softmax
Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, and Jiashi Feng · 2020
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Imbalance problems in object detection: A review
Kemal Oksuz, Baris Can Cam, Sinan Kalkan, and Emre Akbas · 2020
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Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution
Siyuan Qiao, Liang-Chieh Chen, and Alan Yuille · 2020
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Balanced activation for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Zhongang Cai, and Haiyu Zhao · 2020
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Equalization loss for long-tailed object recognition
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan · 2020
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1st place solution of lvis challenge 2020: A good box is not a guarantee of a good mask
Jingru Tan, Gang Zhang, Hanming Deng, Changbao Wang, Lewei Lu, Quanquan Li, and Jifeng Dai · 2020
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Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V Le · 2020
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Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
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Scaled-yolov4: Scaling cross stage partial network
Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao · 2020
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The devil is in classification: A simple framework for long-tail object detection and instance segmentation
Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Junhao Liew, Sheng Tang, Steven Hoi, and Jiashi Feng · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, and Alexander Smola · 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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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V Le · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, and Quoc V. Le · 2020
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