Foreground Segmentation

Foreground segmentation, the task of isolating objects of interest from their background in images or videos, is a crucial area in computer vision with applications ranging from medical image analysis to autonomous driving. Current research emphasizes improving accuracy and robustness across diverse scenarios, including challenging conditions like atmospheric turbulence and complex backgrounds, often employing deep learning architectures such as U-Nets and transformers, along with techniques like visual prompting and adversarial learning to enhance performance. These advancements are driving progress in various fields, enabling more accurate object detection, improved image editing tools, and more reliable autonomous systems.