The hybrid of Convolutional Neural Network (CNN) and Vision Transformers (ViT) architectures has emerged as a groundbreaking approach, pushing the boundaries of computer vision (CV). This comprehensive review provides a thorough examination of the literature on state-of-the-art hybrid CNN-ViT architectures, exploring the synergies between these two approaches. The main content of this survey includes: (1) a background on the vanilla CNN and ViT, (2) systematic review of various taxonomic hybrid designs to explore the synergy achieved through merging CNNs and ViTs models, (3) comparative analysis and application task-specific synergy between different hybrid architectures, (4) challenges and future directions for hybrid models, (5) lastly, the survey concludes with a summary of key findings and recommendations. Through this exploration of hybrid CV architectures, the survey aims to serve as a guiding resource, fostering a deeper understanding of the intricate dynamics between CNNs and ViTs and their collective impact on shaping the future of CV architectures.

综述探讨了卷积神经网络（CNN）与视觉Transformer（ViT）架构的混合体，深入检查了最新的混合CNN-ViT架构文献，探索了这两种方法之间的协同作用。通过对混合CV架构的研究，旨在作为一个指导资源，促进对CNN和ViT之间错综复杂的动态关系及其对CV架构未来的共同影响的更深入的理解。

探索混合CNN和视觉转换器的协同效应：计算机视觉的综述