Saliency Map Generation

Saliency map generation aims to visualize which parts of an input (e.g., image, point cloud) are most influential in a model's prediction, enhancing the explainability of complex AI systems. Current research focuses on improving the accuracy and efficiency of saliency map generation, particularly for "black-box" models and challenging data types like point clouds, employing techniques like gradient-based methods, perturbation methods, and novel fusion strategies to enhance existing approaches. These advancements are crucial for building trust in AI, enabling better model debugging and design, and facilitating applications in diverse fields such as medical image analysis and geospatial AI.