A dual-balanced network for long-tail distribution object detection

Object detection on datasets with imbalanced distributions (i.e. long-tail distributions) dataset is a significantly challenging task. Some re-balancing solutions, such as re-weighting and re-sampling have two main disadvantages. First, re-balancing strategies only utilise a coarse-grained global threshold to suppress some of the most influential categories, while overlooking locally influential categories. Second, very few studies have specifically designed algorithms for object detection tasks under long-tail distribution. To address these two issues, a dual-balanced network for fine-grained re-balancing object detection is proposed. Our re-balancing strategies are both in proposal and classification logic, corresponding to two sub-networks, the Balance Region Proposal Network (BRPN) and the Balance Classification Network (BCN). The BRPN sub-network equalises the number of proposals in the background and foreground by reducing the sampling probability of simple backgrounds, and the BCN sub-network equalises the logic between head and tail categories by globally suppressing negative gradients and locally fixing the over-suppressed negative gradients. In addition, the authors advise a balance binary cross entropy loss to jointly re-balance the entire network. This design can be generalised to different two-stage object detection frameworks. The experimental mAP result of 26.40% on this LVIS-v0.5 dataset outperforms most SOTA methods.