Visual Interpretation

Visual interpretation aims to make the decision-making processes of complex machine learning models, particularly in computer vision, more transparent and understandable to humans. Current research focuses on developing novel algorithms and model architectures, such as those based on class activation maps (CAMs), neural additive models (NAMs), and multi-agent frameworks, to generate more accurate and informative visual explanations. This work is crucial for building trust in AI systems, improving model debugging and design, and enabling effective human-computer collaboration in diverse applications ranging from medical diagnosis to autonomous systems.