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 CRMobileNetV2 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.
<Strengths> 
+ This paper performs architecture search for object detection, especially for the computation allocation across different resolutions. It is a new application for NAS research. 
+ The proposed approach shows some marginal improvement of object detection accuracy across multiple backbones and datasets. 
+ This work proposes a new formulation to apply NAS approach to object detectors, including linking between the ERF and the computation allocation of backbone and two-level hierarchical search for stages and convolution operations.
<Weakness>
1. This paper can be regarded as an engineering work for a new domain of problem with little technical novelty. 
- From the perspective of NAS research, the proposed approach has little technical novelty; instead, it seems like an application of existing techniques (or even simpler ones) (e.g.  to a new domain of problem - object detection. 
- Given that the NAS is only applied to CNN backbones in this paper, the novelty (of proposing a new task) may be further weakened.
- The novelty of this work over existing works of “NAS on detection” (Chen et al 2019, Wang et al 2019 and Ghiasi et al 2019) is not justified.
2. Experimental results are rather weak. 
- This paper only focuses on showing that the proposed method marginally improves the performance of basic backbones (MobileNet V2 and ResNet 18/50/101). However, the improvement gaps are rather marginal (about 1.0% in average as shown in Fig.5).
- This paper does not compare its performance with other SOTA detection methods but only compare with some baselines instead. However, the performances of baselines are too low; for example, in Table 1, the mAP of the ResNet101 baseline is 38.6, which is lower by about 10 than the SOTA detector with the same ResNet101+FRCNN+FPN. It may not be convincing to use the baselines that have more than 20% lower accuracy compared to SOTA and show only ~1.x improvement over it.
- Given that the proposed approach is applicable to any backbones and detectors, more experiments should be done using recent stronger baselines (including many recent tricks like RoIAlign and DCN).
- Another experimental weakness is lack of comparison with existing NAS methods on object detections such as (Chen et al 2019, Wang et al 2019 and Ghiasi et al 2019). Even though experiment settings here may be different from those of these papers,  they should be compared in any reasonable ways. Moreover, the detection accuracies reported in this paper are not as good as the numbers of these papers. This paper argues that the proposed approach is complementary to these methods, so it would be good to report the results of the combined model with them.
3. The paper is written poorly.
- There are many grammatically wrong and awkward expressions. The draft should be thoroughly proofread. 
<Conclusion>
Although this work shows a new application of NAS for object detection, my initial decision is ‘weak reject’ mainly due to lack of technical novelty, limited experiments and poor writing. 
<Post-rebuttal comments>
Authors' responses partly resolved my concerns on the experiments. I have no object to accept this paper.