Tracing the trajectory of innovation in image captioning: A journey from sequential simplicity to multimodal mastery.
Language Models
This study explores the advancements in automatic image captioning by comparing three seq2seq models: the foundational Merge Network, the Encoder/Decoder with Attention, and the cutting-edge OFA model. Employing both quantitative BLEU scores and qualitative assessments, the research highlights the evolution from basic seq2seq frameworks to sophisticated multi-modal architectures. The results showcase a clear progression in the field, demonstrating significant improvements in the accuracy and complexity of generated image captions. This comparative analysis not only validates the rapid development in image captioning techniques but also emphasizes the shift towards more advanced, nuanced AI models in this domain.