Visual Inference

Visual inference research focuses on how humans and machines interpret visual information, aiming to understand and replicate the processes of perception, reasoning, and decision-making based on visual data. Current research explores diverse approaches, including Bayesian methods, predictive coding models (both hybrid and iterative), and novel architectures like those combining code generation with vision-language models. These efforts aim to improve the accuracy, robustness, and efficiency of visual inference in artificial systems, with implications for robotics, computer vision, and a deeper understanding of human cognition.