Visual Explanation

Visual explanation aims to make the decision-making processes of complex machine learning models, particularly deep neural networks (DNNs), more transparent and understandable. Current research focuses on developing and improving techniques like Class Activation Maps (CAMs) and their variants, leveraging attention mechanisms in Vision Transformers (ViTs), and integrating multimodal approaches combining visual and textual explanations. This field is crucial for building trust in AI systems, enabling better model debugging and bias detection, and facilitating more effective human-computer interaction in diverse applications such as medical diagnosis and recommender systems.