Balanced Classification: A Unified Framework for Long-Tailed Object
  Detection
Tianhao Qi Hongtao Xie Pandeng Li Jiannan Ge Yongdong Zhang
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.