Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection
Burhaneddin Yaman Tanvir Mahmud Chun-Hao Liu
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.