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 sub-optimal 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: this https URL

本文提出一种 Gumbel 优化损失函数，用于解决深度检测器中 Sigmoid 或 Softmax 函数导致检测稀有类别低效的问题，实验结果表明该方法能显著提高罕见类别的检测效果。

使用关于 Gumbel 优化的损失函数进行长尾实例分割

Long-tailed Instance Segmentation using Gumbel Optimized Loss

The conventional detectors tend to make imbalanced classification and suffer
performance drop, when the distribution of the training data is severely
skewed. In this paper, we propose to use the mean classification score to
indicate the classification accuracy for each category during training. Based
on this indicator, we balance the classification via an Equilibrium Loss (EBL)
and a Memory-augmented Feature Sampling (MFS) method. Specifically, EBL
increases the intensity of the adjustment of the decision boundary for the weak
classes by a designed score-guided loss margin between any two classes. On the
other hand, MFS improves the frequency and accuracy of the adjustment of the
decision boundary for the weak classes through over-sampling the instance
features of those classes. Therefore, EBL and MFS work collaboratively for
finding the classification equilibrium in long-tailed detection, and
dramatically improve the performance of tail classes while maintaining or even
improving the performance of head classes. We conduct experiments on LVIS using
Mask R-CNN with various backbones including ResNet-50-FPN and ResNet-101-FPN to
show the superiority of the proposed method. It improves the detection
performance of tail classes by 15.6 AP, and outperforms the most recent
long-tailed object detectors by more than 1 AP. Code is available at
this https URL

本研究提出使用平均分类得分来指示每个类别的分类准确性，并通过平衡损失和记忆增强特征采样方法，在长尾检测中找到分类平衡，显著提高了尾部类的检测性能，且代码可用。

探索长尾目标检测中的分类平衡

Exploring Classification Equilibrium in Long-Tailed Object Detection

Training on datasets with long-tailed distributions has been challenging for
major recognition tasks such as classification and detection. To deal with this
challenge, image resampling is typically introduced as a simple but effective
approach. However, we observe that long-tailed detection differs from
classification since multiple classes may be present in one image. As a result,
image resampling alone is not enough to yield a sufficiently balanced
distribution at the object level. We address object-level resampling by
introducing an object-centric memory replay strategy based on dynamic, episodic
memory banks. Our proposed strategy has two benefits: 1) convenient
object-level resampling without significant extra computation, and 2) implicit
feature-level augmentation from model updates. We show that image-level and
object-level resamplings are both important, and thus unify them with a joint
resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed
detection and segmentation methods on LVIS v0.5 across various backbones. Code
is available at this https URL

该研究针对长尾分布数据集的检测问题，引入了基于对象中心的记忆重放策略，采用动态的场景式记忆库来实现对象级别重新采样，进而实现图像级别和对象级别的联合采样策略，提出的方法在 LVIS v0.5 上超过当前长尾检测和分割方法的状态。