Recent region-based object detectors are usually built with separate
classification and localization branches on top of shared feature extraction
networks. In this paper, we analyze failure cases of state-of-the-art detectors
and observe that most hard false positives result from classification instead
of localization. We conjecture that: (1) Shared feature representation is not
optimal due to the mismatched goals of feature learning for classification and
localization; (2) multi-task learning helps, yet optimization of the multi-task
loss may result in sub-optimal for individual tasks; (3) large receptive field
for different scales leads to redundant context information for small
objects.We demonstrate the potential of detector classification power by a
simple, effective, and widely-applicable Decoupled Classification Refinement
(DCR) network. DCR samples hard false positives from the base classifier in
Faster RCNN and trains a RCNN-styled strong classifier. Experiments show new
state-of-the-art results on PASCAL VOC and COCO without any bells and whistles.

本文提出了一种针对现有目标检测器分类失败问题设计的基于多任务学习的解决方案，其中使用一种 Decoupled Classification Refinement（DCR）网络对分类错误进行有效的样本采样和重新分类。