Fetching the paper…
Reading the bibliography…
The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
Smoteboost: Improving prediction of the minority class in boosting
Nitesh V Chawla, Aleksandar Lazarevic, Lawrence O Hall, and Kevin W Bowyer · 2003
Earlier work this paper cites.
C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling
Chris Drummond, Robert C Holte, et al · 2003
Earlier work this paper cites.
A multiple resampling method for learning from imbalanced data sets
Andrew Estabrooks, Taeho Jo, and Nathalie Japkowicz · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
Earlier work this paper cites.
Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
Earlier work this paper cites.
Bayesian optimization: Open source constrained global optimization tool for python
Fernando Nogueira et al · 2014
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 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.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
The devil is in the tails: Fine-grained classification in the wild
Grant Van Horn and Pietro Perona · 2017
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Cited alongside, same era.
Deepncm: Deep nearest class mean classifiers
Samantha Guerriero, Barbara Caputo, and Thomas Mensink · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
Cited alongside, same era.
Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al · 2020
Later among the works it cites.
Long-tailed recognition using class-balanced experts
Saurabh Sharma, Ning Yu, Mario Fritz, and Bernt Schiele · 2020
Later among the works it cites.
Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu · 2020
Later among the works it cites.
Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification
Liuyu Xiang, Guiguang Ding, and Jungong Han · 2020
Later among the works it cites.
Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2020
Later among the works it cites.
Parametric contrastive learning
Jiequan Cui, Zhisheng Zhong, Shu Liu, Bei Yu, and Jiaya Jia · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
Cited alongside, same era.
Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
Cited alongside, same era.
Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2019
Cited alongside, same era.
f-vaegan-d2: A feature generating framework for any-shot learning
Yongqin Xian, Saurabh Sharma, Bernt Schiele, and Zeynep Akata · 2019
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
Cited alongside, same era.
Later among the works it cites.
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
Cong Fang, Hangfeng He, Qi Long, and Weijie J Su · 2021
Later among the works it cites.
Gistnet: a geometric structure transfer network for long-tailed recognition
Bo Liu, Haoxiang Li, Hao Kang, Gang Hua, and Nuno Vasconcelos · 2021
Later among the works it cites.
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2021
Later among the works it cites.
Distributional robustness loss for long-tail learning
Dvir Samuel and Gal Chechik · 2021
Later among the works it cites.
Distribution alignment: A unified framework for long-tail visual recognition
Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, and Jian Sun · 2021
Later among the works it cites.
Long-tailed recognition via weight balancing
Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, and Shu Kong · 2022
Later among the works it cites.
Imbalance trouble: Revisiting neural-collapse geometry
Christos Thrampoulidis, Ganesh Ramachandra Kini, Vala Vakilian, and Tina Behnia · 2022
Later among the works it cites.