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Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining by Jiang, Chiyu Max; Najibi, Mahyar; Qi, Charles R.; Zhou, Yin; Anguelov, Dragomir is a scholarly article available to read on EtoBox.

What is Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining about?

Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples. However, even the best performing models suffer from the most naive mistakes when it comes to rare examples that do not appear frequently in the training data, such as vehicles with irregular geometries. Most studies in the long-tail literature focus on class-imbalanced classification problems with known imbalanced label counts per class, but they are not directly applicable to the intra-class long-tail examples in problems with large intra-class variations such as 3D object detection, where instances with the same class label can have drastically varied properties such as shapes and sizes. Other works propose to mitigate this problem using active learning based on the criteria of uncertainty, difficulty, or diversity. In this study, we identify a new conceptual dimension - rareness - to mine new data for improving the long-tail performance of models. We show that rareness, as opposed to difficulty, is the key to data-centric improvem

Author

Jiang, Chiyu Max; Najibi, Mahyar; Qi, Charles R.; Zhou, Yin; Anguelov, Dragomir

Published

2022

Language

EN