Pixels to Prose: Understanding the art of Image Captioning

TL;DR

This paper reviews the evolution of image captioning techniques, from basic models to advanced solutions, emphasizing their applications, evaluation metrics, and significance in fields like medicine.

Contribution

It provides a comprehensive overview of image captioning methods, including architectures, evaluation metrics, and real-world applications, especially in the medical domain.

Findings

Dissects various image captioning architectures and their mechanisms.

Highlights the importance of evaluation metrics for system performance.

Explores applications of image captioning in medical scenarios.

Abstract

In the era of evolving artificial intelligence, machines are increasingly emulating human-like capabilities, including visual perception and linguistic expression. Image captioning stands at the intersection of these domains, enabling machines to interpret visual content and generate descriptive text. This paper provides a thorough review of image captioning techniques, catering to individuals entering the field of machine learning who seek a comprehensive understanding of available options, from foundational methods to state-of-the-art approaches. Beginning with an exploration of primitive architectures, the review traces the evolution of image captioning models to the latest cutting-edge solutions. By dissecting the components of these architectures, readers gain insights into the underlying mechanisms and can select suitable approaches tailored to specific problem requirements without duplicating efforts. The paper also delves into the application of image captioning in the medical domain, illuminating its significance in various real-world scenarios. Furthermore, the review offers guidance on evaluating the performance of image captioning systems, highlighting key metrics for assessment. By synthesizing theoretical concepts with practical application, this paper equips readers with the knowledge needed to navigate the complex landscape of image captioning and harness its potential for diverse applications in machine learning and beyond.