Robust Visual Perception

Robust visual perception aims to develop computer vision systems that reliably interpret images and videos even under challenging conditions, mirroring human resilience to variations in lighting, noise, and other real-world factors. Current research focuses on improving the robustness of various models, including vision-language foundational models, neural radiance fields (NeRFs), and convolutional neural networks, often through techniques like data augmentation with synthetically generated images (e.g., using diffusion models) and the development of specialized filters (e.g., object permanence filters). These advancements are crucial for enhancing the reliability of applications such as autonomous driving, underwater robotics, and human-robot interaction, where robust perception is paramount for safe and effective operation.