  The allocation of computation resources in the backbone is a crucial issue in
object detection. However, classification allocation pattern is usually adopted
directly to object detector, which is proved to be sub-optimal. In order to
reallocate the engaged computation resources in a more efficient way, we
present CR-NAS (Computation Reallocation Neural Architecture Search) that can
learn computation reallocation strategies across different feature resolution
and spatial position diectly on the target detection dataset. A two-level
reallocation space is proposed for both stage and spatial reallocation. A novel
hierarchical search procedure is adopted to cope with the complex search space.
We apply CR-NAS to multiple backbones and achieve consistent improvements. Our
CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9% and 1.7% COCO
AP respectively without any additional computation budget. The models
discovered by CR-NAS can be equiped to other powerful detection neck/head and
be easily transferred to other dataset, e.g. PASCAL VOC, and other vision
tasks, e.g. instance segmentation. Our CR-NAS can be used as a plugin to
improve the performance of various networks, which is demanding.
