YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
The discussion on a new AI framework explores how to improve object detection reliability using YOLO's efficiency combined with Kolmogorov-Arnold networks (KAN) and vision-language models (BLIP). The goal is to move beyond opaque 'black box' systems, providing trustworthy, interpretable results for safety-critical applications.