2019

Computation Reallocation for Object Detection

Liang, Feng, Lin, Chen, Guo, Ronghao et al.

Understand

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.

Built on

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