Local and Global Logit Adjustments for Long-Tailed Learning-Reference-Cited by-同舟云学术

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Local and Global Logit Adjustments for Long-Tailed Learning

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Tao Yingfan¹,Sun Jingna²,Yang Hao²,Chen Li²,Wang Xu²,Yang Wenming¹,Du Daniel²,Zheng Min²

Affiliation:

1. Tsinghua University

2. ByteDance Inc

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378502.pdf?arnumber=10378502

Reference51 articles.

1. ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

2. Learning imbalanced datasets with label-distribution-aware margin loss;Cao;Advances in neural information processing systems,2019

3. SMOTE: Synthetic Minority Over-sampling Technique

4. Feature Space Augmentation for Long-Tailed Data

5. Randaugment: Practical automated data augmentation with a reduced search space

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FSODv2: A Deep Calibrated Few-Shot Object Detection Network;International Journal of Computer Vision;2024-04-04

2. Consensus and Risk Aversion Learning in Ensemble of Multiple Experts for Long-Tailed Classification;IEEE Access;2024

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