Hybrid Attention-Enhanced CNN–Transformer Framework for Next-Generation Image Classification
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Image classification, a cornerstone of computer vision, has been significantly advanced by deep learning models. Convolutional Neural Networks (CNNs) have long been the gold standard due to their powerful inductive biases for capturing local features and spatial hierarchies. More recently, Vision Transformers (ViTs) have emerged as a compelling alternative, leveraging self-attention mechanisms to model long-range dependencies and global context. However, both architectures possess inherent limitations: CNNs struggle with global context, while ViTs lack the spatial inductive biases of convolutions and often require extensive training data. This chapter introduces a novel Hybrid Attention-Enhanced CNN–Transformer Framework that synergistically combines the strengths of both paradigms. Our proposed architecture integrates a CNN backbone for robust local feature extraction with a multi-head self-attention module to capture global contextual information. By vertically stacking and fusing these components in a principled manner, the framework achieves superior performance while maintaining computational efficiency. We evaluate the proposed model on the CIFAR- dataset, demonstrating state-of-the-art accuracy that surpasses both pure CNN and ViT baselines. The chapter provides a comprehensive analysis of the architecture, training dynamics, and performance, including detailed discussions on the model’s interpretability through attention visualization. The results underscore the potential of hybrid models to define the next generation of image classification systems.
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