Recently, large-scale language-image generative models have gained widespread
attention and many works have utilized generated data from these models to
further enhance the performance of perception tasks. However, not all generated
data can positively impact downstream models, and these methods do not
thoroughly explore how to better select and utilize generated data. On the
other hand, there is still a lack of research oriented towards active learning
on generated data. In this paper, we explore how to perform active learning
specifically for generated data in the long-tailed instance segmentation task.
Subsequently, we propose BSGAL, a new algorithm that online estimates the
contribution of the generated data based on gradient cache. BSGAL can handle
unlimited generated data and complex downstream segmentation tasks effectively.
Experiments show that BSGAL outperforms the baseline approach and effectually
improves the performance of long-tailed segmentation. Our code can be found at
this https URL

本文探讨了如何在长尾实例分割任务中专门对生成数据执行主动学习，并提出了一种新的基于梯度缓存的算法 BSGAL，可以有效处理无限的生成数据和复杂的下游分割任务。实验证明，BSGAL 优于基线方法，有效提高了长尾分割的性能。

长尾实例分割的生成式主动学习

Generative Active Learning for Long-tailed Instance Segmentation

Long-tailed instance segmentation is a challenging task due to the extreme
imbalance of training samples among classes. It causes severe biases of the
head classes (with majority samples) against the tailed ones. This renders "how
to appropriately define and alleviate the bias" one of the most important
issues. Prior works mainly use label distribution or mean score information to
indicate a coarse-grained bias. In this paper, we explore to excavate the
confusion matrix, which carries the fine-grained misclassification details, to
relieve the pairwise biases, generalizing the coarse one. To this end, we
propose a novel Pairwise Class Balance (PCB) method, built upon a confusion
matrix which is updated during training to accumulate the ongoing prediction
preferences. PCB generates fightback soft labels for regularization during
training. Besides, an iterative learning paradigm is developed to support a
progressive and smooth regularization in such debiasing. PCB can be plugged and
played to any existing method as a complement. Experimental results on LVIS
demonstrate that our method achieves state-of-the-art performance without bells
and whistles. Superior results across various architectures show the
generalization ability. The code and trained models are available at
this https URL

本研究探讨使用混淆矩阵对长尾实例分割问题中不同类别间的精细误分类信息进行建模，从而解决训练样本不平衡引起的模型偏差问题，提出的 Pairwise Class Balance (PCB) 方法能够有效地进行模型规范化训练，实验结果表明该方法具有较强的泛化性能和优越的表现。

通过配对类别平衡来缓解长尾实例分割问题

Relieving Long-tailed Instance Segmentation via Pairwise Class Balance

Recent methods for long-tailed instance segmentation still struggle on rare
object classes with few training data. We propose a simple yet effective
method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the
data scarcity issue by augmenting the feature space especially for rare
classes. Both the Feature Augmentation (FA) and feature sampling components are
adaptive to the actual training status -- FA is informed by the feature mean
and variance of observed real samples from past iterations, and we sample the
generated virtual features in a loss-adapted manner to avoid over-fitting. FASA
does not require any elaborate loss design, and removes the need for
inter-class transfer learning that often involves large cost and
manually-defined head/tail class groups. We show FASA is a fast, generic method
that can be easily plugged into standard or long-tailed segmentation
frameworks, with consistent performance gains and little added cost. FASA is
also applicable to other tasks like long-tailed classification with
state-of-the-art performance.

我们提出了一种名为 FASA（Feature Augmentation and Sampling Adaptation）的方法，通过对稀有类别的特征空间进行增强以解决数据稀缺问题，该方法适用于标准或长尾分割框架，并可适用于其他任务，如长尾分类。