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。