Tail Classes Matter: Long-Tailed Object Detection Revisited-Reference-Cited by-同舟云学术

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Tail Classes Matter: Long-Tailed Object Detection Revisited

Published:2024-04-14 Issue: Volume: Page:3140-3144
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
language:
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Author:

Zhang Yinglu¹,Zhang Chenbo¹,Zhang Lu¹,Liu Tianying²,Guan Jihong²,Liang Xinkai³,Zhao Jiajia³,Zhou Shuigeng¹

Affiliation:

1. Fudan University,Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science,China

2. Tongji University,Department of Computer Science & Technology,China

3. Beijing Electro-Mechanical Engineering Institute,Science and Technology on Complex System Conroland InelligentAgent Cooperation Laboratory,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446683.pdf?arnumber=10446683

Reference20 articles.

1. The Pareto, Zipf and other power laws

2. LVIS: A Dataset for Large Vocabulary Instance Segmentation

3. Image-level or object-level? a tale of two re-sampling strategies for long-tailed detection;Chang

4. The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation

5. Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation

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