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Major advancements have been made in the field of object detection andsegmentation recently. However, when it comes to rare categories, thestate-of-the-art methods fail to detect them, resulting in a significantperformance gap between rare and frequent categories. In this paper, weidentify that Sigmoid or Softmax functions used in deep detectors are a majorreason for low performance and are sub-optimal for long-tailed detection andsegmentation. To address this, we develop a Gumbel Optimized Loss (GOL), forlong-tailed detection and segmentation. It aligns with the Gumbel distributionof rare classes in imbalanced datasets, considering the fact that most classesin long-tailed detection have low expected probability. The proposed GOLsignificantly outperforms the best state-of-the-art method by 1.1% on AP , andboosts the overall segmentation by 9.0% and detection by 8.0%, particularlyimproving detection of rare classes by 20.3%, compared to Mask-RCNN, on LVISdataset. Code available at: https://github.com/kostas1515/GOL

Long-tailed Instance Segmentation using Gumbel Optimized Loss