The rapid advancement of deep learning models (DLMs) in computer vision is driven by their capacity to unify feature representation and task inference into end-to-end automated frameworks, thereby surpassing manual feature engineering. This survey categorizes DLMs into Convolutional Neural Networks (CNNs), Visual Transformers (ViTs), and hybrid models based on their core components (convolutional vs. attention-based), systematically reviews their technical evolution, and critically analyzes their strengths and limitations. CNNs, the pioneering paradigm, leverage the locality of convolution kernels for efficient hierarchical pattern extraction. However, their reliance on multi-layer stacking to aggregate global context and rigidity to geometric variations has motivated innovations such as deformable convolutions to expand receptive fields. ViTs, emerging later, prioritize the globality of attention mechanisms to model long-range dependencies directly, but face computational bottlenecks and noise sensitivity, which are addressed by sparse attention and hierarchical tokenization. Hybrid models now dominate as optimal compromises, combining the locality of CNNs and globality of ViTs to balance efficiency and expressiveness. Moreover, we emphasize universal techniques—neural architecture search (NAS), model compression, and self-supervised learning—as indispensable tools to enhance robustness and adaptability across architectures. Despite progress, challenges persist in interpretability, data efficiency, and real-world robustness. We advocate prioritizing lightweight hybrid designs, explainable attention-convolution combinations, and cross-modal systems integrating more data beyond vision. This survey not only systematically reviews the technical evolution of DLMs but also proposes a forward-looking roadmap for adaptive, scalable, and trustworthy vision systems, grounded in current technological trajectories.
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