Long-Tailed Recognition Using Class-Balanced Experts

Sharma, Saurabh and Yu, Ning and Fritz, Mario and Schiele, Bernt
(2020) Long-Tailed Recognition Using Class-Balanced Experts.
In: German Conference on Pattern Recognition (GCPR).
Conference: GCPR German Conference on Pattern Recognition

Full text not available from this repository.
Official URL: https://doi.org/10.1007/978-3-030-71278-5_7


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.


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