Enhancing Cost-Efficient Image Captioning Through ExpansionNet v2 Optimization

This paper proposes a novel approach to enhance the cost-efficiency of image captioning systems by optimizing ExpansionNet v2, a state-of-the-art deep model that presents superior performance in generating accurate image captions. Based on leveraging techniques in model compression and efficiency improvement, we introduce some cutting-edge improvements to ExpansionNet v2 to significantly reduce computational complexity and resource requirements without compromising captioning quality. Through extensive experimentation on benchmark datasets, including Microsoft COCO, our optimized ExpansionNet v2 variant demonstrates remarkable efficiency gains while maintaining competitive performance across various different metrics and captioning accuracy. The proposed method not only advances the practicality of image captioning systems in resource-constrained environments but also contributes to the broader goal of sustainable AI development.