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论文类型：期刊论文

第一作者：Sun,Yuzhu,Fang,Jiemin

通讯作者：Wang,Xinggang

合写作者：Liu,Wenyu,Li,Yuan,Peng,Kangjian,Zhang,Qian

发表刊物：IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)

DOI码：10.1109/TPAMI.2020.3044416

发表时间：2020-12-14

影响因子：17.861

摘要：Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be achieved by designing network architectures specifically for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation. One major challenge though is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost. In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e.g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation/detection tasks a lot more efficiently. In our experiments, we conduct FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation that clearly outperform existing networks designed both manually and by NAS. We also implement FNA++ on ResNets and NAS networks, which demonstrates a great generalization ability. FNA++ takes far less computation cost than other methods.