| Summary: | In recent years, with the rapid development of deep learning, deformable convolutional networks have received extensive attention because of their powerful feature extraction capabilities, overcoming some problems that are difficult to solve in convolutional neural networks, and have played an important role in computer vision, natural language processing and other related fields. Since there is a little research on systematic summary of the deformable convolutional network, in order to provide a detailed reference for subsequent research, this paper summarizes the related work since the introduction of the deformable convolutional network. Firstly, this paper reviews the high-quality literature in recent years, and introduces the core technologies such as deformable convolution and deformable region of interest pooling in deformable convolutional networks from the perspective of invariant features. Secondly, the collected relevant literature is classified according to different research fields, and the appli-cation of deformable convolutional networks in image recognition and classification, target detection, image seg-mentation, target tracking and other research fields is comprehensively summarized. At the same time, the perfor-mance, advantages and disadvantages of important network models are listed. Thirdly, by combing the literature, the advantages and disadvantages of the deformable convolutional network are analyzed, and the possible future research trends of the deformable convolutional network are discussed according to some problems existing at the present stage. Finally, the deformable convolutional networks are summarized and prospected based on invariant feature extraction.
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