High Quality Instance Segmentation

High-quality instance segmentation aims to accurately delineate and classify individual objects within images, a crucial task in computer vision with applications ranging from medical image analysis to autonomous driving. Current research focuses on improving accuracy and efficiency through various approaches, including novel network architectures (e.g., transformer-based models and efficient CNNs), refined loss functions, and active learning strategies to reduce annotation costs. These advancements are leading to more precise and computationally feasible instance segmentation, impacting fields that rely on accurate object identification and localization.