Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. In this paper, we contend that the learning bias originates from two factors: 1) the unequal competition arising from the imbalanced distribution of foreground categories, and 2) the lack of sample diversity in tail categories. To tackle these issues, we introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution and dynamic intensification of sample diversities in a synchronized manner. Specifically, a novel foreground classification balance loss (FCBL) is developed to ameliorate the domination of head categories and shift attention to difficult-to-differentiate categories by introducing pairwise class-aware margins and auto-adjusted weight terms, respectively. This loss prevents the over-suppression of tail categories in the context of unequal competition. Moreover, we propose a dynamic feature hallucination module (FHM), which enhances the representation of tail categories in the feature space by synthesizing hallucinated samples to introduce additional data variances. In this divide-and-conquer approach, BACL sets a new state-of-the-art on the challenging LVIS benchmark with a decoupled training pipeline, surpassing vanilla Faster R-CNN with ResNet-50-FPN by 5.8% AP and 16.1% AP for overall and tail categories. Extensive experiments demonstrate that BACL consistently achieves performance improvements across various datasets with different backbones and architectures. Code and models are available at https://github.com/Tianhao-Qi/BACL.

翻译：传统检测器在处理长尾数据时，由于对头部多数类别的分类偏差，性能会下降。本文认为学习偏差源于两个因素：1）前景类别分布不均衡导致的不平等竞争，以及2）尾部类别缺乏样本多样性。为解决这些问题，我们提出统一框架BACL（平衡分类），该框架能够自适应地修正因类别分布差异导致的不平等性，并同步动态增强样本多样性。具体而言，我们设计了新型前景分类平衡损失（FCBL），通过引入逐对类别感知边界和自动调整权重项，分别缓解头部类别的支配效应并将注意力转向难区分类别。该损失可防止尾部类别在不平等竞争中被过度抑制。此外，我们提出动态特征幻觉模块（FHM），通过合成幻觉样本引入额外数据方差，增强特征空间中尾部类别的表征能力。采用这种分治策略，BACL在具有挑战性的LVIS基准上利用解耦训练流程创下新纪录，在整体和尾部类别上分别超越使用ResNet-50-FPN的vanilla Faster R-CNN达5.8% AP和16.1% AP。大量实验表明，BACL在不同主干网络和架构的多个数据集上均能持续提升性能。代码与模型已开源至https://github.com/Tianhao-Qi/BACL。