Stack-Based Ensembles for Robust Neural Image Captioning

This paper considers a stack-based ensemble approach to image captioning which will augment the textual description generation task from visual input in an accurate and contextually relevant manner. Diverse architectures such as Convolutional Neural Networks coupled with LSTM networks and models of Transformer, along with Graph Neural Networks, give leeway over the limitations of single-model approaches that have been standing against managing the complex visual data. Results obtained on established datasets like MS COCO, Flickr30k, and Flickr8k illustrate the benefit of ensemble over single models, resulting in the BLEU-4 score of 0.67, METEOR score of 0.34, and CIDEr of 1.20. This research impacts not only applications but also life: from real-time social media captioning, creation of personalized content, up to accessibility solutions. This work, therefore, puts emphasis on the vast potential of ensemble learning regarding advancing image captioning technologies and, hence, locates it as a promising area for future exploration in both digital media and assistive technologies.