Explainable Image

Explainable image analysis aims to develop image processing and retrieval methods that not only produce accurate results but also provide clear, understandable justifications for those results. Current research focuses on integrating explainable AI techniques, such as gradient-based explanations, layer-wise relevance propagation, and attention mechanisms, with various model architectures including Siamese networks, transformers, and graph neural networks, to achieve this goal. This work is significant because it addresses the "black box" nature of many deep learning models, improving trust, transparency, and ultimately, the usability of image-based systems across diverse applications like product question answering, medical image analysis, and art historical research. The development of more interpretable models also facilitates better model debugging and refinement.