The performance of object detectors has been considerably improved by various designs ofarchitecture, lossfunctionetc.

We also tried to utilize these tricks for training CNN-based object detectors. As shown in Table B, these tricks bring 0.2%-0.6% On contrast, this strategy improves the adder detector for 1.2% mAP, which indicates that the It is an interesting topic to explore the robustness to the domain shift for AdderNet-based detector. Figure 1: Qualitative results of RetinaNet [2], FCOS [3] and our proposed Adder FCOS. As shown in Table C, Adder FCOS suffers from 2.2% mAP drop on Cityscapes compared with convolutional counterpart, which is similar with the performance drop on COCO.

Comparisons with state-of-the-arts are conducted on COCO and P ASCAL VOC benchmarks. Specifically, the proposed Adder FCOS achieves a 37.8% AP on the COCO val set, demonstrating comparable performance