posted on 2023-11-29, 18:15authored bySaurabh Sharma, Ning Yu, Mario FritzMario Fritz, Bernt Schiele
Deep learning enables impressive performance in image recognition using large-scale artificially-balanced datasets. However, real-world datasets exhibit highly class-imbalanced distributions, yielding two main challenges: relative imbalance amongst the classes and data scarcity for mediumshot or fewshot classes. In this work, we address the problem of long-tailed recognition wherein the training set is highly imbalanced and the test set is kept balanced. Differently from existing paradigms relying on data-resampling, cost-sensitive learning, online hard example mining, loss objective reshaping, and/or memory-based modeling, we propose an ensemble of class-balanced experts that combines the strength of diverse classifiers. Our ensemble of class-balanced experts reaches results close to state-of-the-art and an extended ensemble establishes a new state-of-the-art on two benchmarks for long-tailed recognition. We conduct extensive experiments to analyse the performance of the ensembles, and discover that in modern large-scale datasets, relative imbalance is a harder problem than data scarcity. The training and evaluation code is available at this https URL.
History
Preferred Citation
Saurabh Sharma, Ning Yu, Mario Fritz and Bernt Schiele. Long-Tailed Recognition Using Class-Balanced Experts. In: German Conference on Pattern Recognition (GCPR). 2020.
Primary Research Area
Trustworthy Information Processing
Name of Conference
German Conference on Pattern Recognition (GCPR)
Legacy Posted Date
2021-02-18
Open Access Type
Green
BibTeX
@inproceedings{cispa_all_3366,
title = "Long-Tailed Recognition Using Class-Balanced Experts",
author = "Sharma, Saurabh and Yu, Ning and Fritz, Mario and Schiele, Bernt",
booktitle="{German Conference on Pattern Recognition (GCPR)}",
year="2020",
}