Abstract
As we known, most of convolution neural architectures are manually designed. However, they cannot obtain the optimal structures. To address this problem, based on Weighted Feature Pyramid Networks (WFPN), in this paper, we use gaussian kernel to calculate the weight to design a novel method called the Neural Architecture Search Weighted Feature Pyramid Networks (i.e., NAS-WFPN). NAS-WFPN mainly consists of three parts (i.e., top-down pathway, bottom-up pathway and lateral connections) to fuse features across different scales. Experimental results show that NAS-WFPN achieves higher accuracy compared with the existing object detection methods. Specifically, NAS-WFPN increases accuracy by 2.3 AP compared to SSDLite with MobileNetV2 model and gets 49.1 AP, which exceeds NAS-FPN and Mask R-CNN.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61972093, and Grant 61702101, and in part by the Young and Middle-aged Teachers Education and Research Project in Fujian Province under Grant JAT170477.
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Li, X., Xie, Z., Lai, T., Zhao, F., Xu, H., Chen, R. (2021). NAS-WFPN: Neural Architecture Search Weighted Feature Pyramid Networks for Object Detection. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12383. Springer, Cham. https://doi.org/10.1007/978-3-030-68884-4_32
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