Object Segmentation

Object segmentation, the task of partitioning an image or video into meaningful regions corresponding to distinct objects, is a core problem in computer vision with applications ranging from autonomous driving to cultural heritage preservation. Current research emphasizes developing robust and efficient methods, particularly focusing on unsupervised or weakly-supervised approaches to reduce reliance on expensive manual annotation, and exploring the use of large pre-trained models like SAM (Segment Anything Model) and transformers for improved accuracy and generalization across diverse scenarios, including camouflaged objects and challenging lighting conditions. These advancements are driving significant improvements in various fields, including robotics, autonomous systems, and medical image analysis.