Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection

Published: 23 Oct 2023, Last Modified: 13 Nov 2023HeavyTails 2023EveryoneRevisionsBibTeX
Keywords: Long-tailed datasets, Object Detection, Instance Segmentation, Class Imbalance, Re-sampling
TL;DR: We introduce instance-aware repeat factor sampling (IRFS) to unify instance and image counts for re-sampling, addressing the imbalance in long-tailed datasets.
Abstract: We propose an embarrassingly simple method -- instance-aware repeat factor sampling (IRFS) to address the problem of imbalanced data in long-tailed object detection. Imbalanced datasets in real-world object detection often suffer from a large disparity in the number of instances for each class. To improve the generalization performance of object detection models on rare classes, various data sampling techniques have been proposed. Repeat factor sampling (RFS) has shown promise due to its simplicity and effectiveness. Despite its efficiency, RFS completely neglects the instance counts and solely relies on the image count during re-sampling process. However, instance count may immensely vary for different classes with similar image counts. Such variation highlights the importance of both image and instance for addressing the long-tail distributions. Thus, we propose IRFS which unifies instance and image counts for the re-sampling process to be aware of different perspectives of the imbalance in long-tailed datasets. Our method shows promising results on the challenging LVIS v1.0 benchmark dataset over various architectures and backbones, demonstrating their effectiveness in improving the performance of object detection models on rare classes with a relative $+50\%$ average precision (AP) improvement over counterpart RFS. IRFS can serve as a strong baseline and be easily incorporated into existing long-tailed frameworks.
Submission Number: 1
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