Dense Vision Task

Dense vision tasks, encompassing pixel-level predictions like semantic segmentation and depth estimation, aim to extract rich spatial information from images. Current research focuses on developing efficient and generalizable models, employing architectures such as vision transformers and diffusion models, often incorporating multi-task learning and parameter-efficient fine-tuning techniques to handle diverse tasks with limited data. These advancements are crucial for improving the accuracy and efficiency of various applications, including robotics, medical imaging, and autonomous driving, by enabling more robust and versatile visual perception systems.