Abstract: 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.

Key words: feature extraction, deformable convolution, invariance feature, region of interest

摘要： 近年来，随着深度学习的快速发展，可变形卷积网络因其强大的特征提取能力受到广泛关注，克服了卷积神经网络中难以解决的一些问题，并且已在计算机视觉、自然语言处理等相关领域发挥重要作用。由于目前对可变形卷积网络进行系统性总结的研究还很少，为了给后续研究提供详细的参考依据，对可变形卷积网络引入以来的相关工作进行总结。首先，综述了近几年的高质量文献，从不变性特征的角度入手，对可变形卷积网络中的可变形卷积和可变形感兴趣区域池化等核心技术进行介绍。然后，将收集到的相关文献按照研究领域的不同进行分类，全面概括现阶段可变形卷积网络在图像识别和分类、目标检测、图像分割、目标追踪等研究领域的应用情况，同时还对比了重要网络模型的性能和优缺点。其次，通过梳理文献，分析可变形卷积网络存在的优势和不足，并根据现阶段存在的一些问题，探讨可变形卷积网络未来可能的研究趋势。最后，基于不变性特征的提取对可变形卷积网络进行了总结和展望。

关键词: 特征提取, 可变形卷积, 不变性特征, 感兴趣区域