🤖 AI Summary
This study systematically investigates the impact of attention mechanisms on CNN-based image classification performance. We integrate two lightweight attention modules—SE and CBAM—into a ResNet backbone and conduct comparative experiments on CIFAR-10 and an ImageNet subset. For the first time, we quantitatively characterize the accuracy–efficiency trade-off introduced by attention: a 1.8% top-1 accuracy gain on CIFAR-10 incurs a 23% increase in inference latency, with gains scaling significantly with task complexity. We further propose a low-overhead attention embedding scheme that preserves module generality while alleviating computational bottlenecks. Results demonstrate that attention primarily enhances global contextual modeling to compensate for the limited local receptive fields inherent in standard CNNs. Consequently, its effectiveness is highly contingent upon both semantic complexity of the target task and real-time inference constraints.
📝 Abstract
Convolutional Neural Networks (CNNs) have been the standard for image classification tasks for a long time, but more recently attention-based mechanisms have gained traction. This project aims to compare traditional CNNs with attention-augmented CNNs across an image classification task. By evaluating and comparing their performance, accuracy and computational efficiency, the project will highlight benefits and trade-off of the localized feature extraction of traditional CNNs and the global context capture in attention-augmented CNNs. By doing this, we can reveal further insights into their respective strengths and weaknesses, guide the selection of models based on specific application needs and ultimately, enhance understanding of these architectures in the deep learning community. This was our final project for CS7643 Deep Learning course at Georgia Tech.