Long-Tailed Instance Segmentation Using Gumbel Optimized Loss
Konstantinos Panagiotis Alexandridis, Jiankang Deng, Anh Nguyen, Shan Luo
Abstract
"Major advancements have been made in the field of object detection and segmentation recently. However, when it comes to rare categories, the state-of-the-art methods fail to detect them, resulting in a significant performance gap between rare and frequent categories. In this paper, we identify that Sigmoid or Softmax functions used in deep detectors are a major reason for low performance and are suboptimal for long-tailed detection and segmentation. To address this, we develop a Gumbel Optimized Loss (GOL), for long-tailed detection and segmentation. It aligns with the Gumbel distribution of rare classes in imbalanced datasets, considering the fact that most classes in long-tailed detection have low expected probability. The proposed GOL significantly outperforms the best state-of-the-art method by 1.1% on AP, and boosts the overall segmentation by 9.0% and detection by 8.0%, particularly improving detection of rare classes by 20.3%, compared to Mask-RCNN, on LVIS dataset. Code available at: https://github.com/kostas1515/ GOL."
BibTeX
@inproceedings{eccv2022_longtailedinstan,
title = {Long-Tailed Instance Segmentation Using Gumbel Optimized Loss},
author = {Konstantinos Panagiotis Alexandridis and Jiankang Deng and Anh Nguyen and Shan Luo},
booktitle = {ECCV 2022},
year = {2022}
}