Segmentation Module
Segmentation modules are crucial components in computer vision systems, aiming to accurately delineate objects or regions of interest within images or videos. Current research focuses on improving segmentation accuracy and efficiency, particularly in challenging scenarios like weakly supervised learning and domain generalization, often leveraging transformer-based architectures and foundation models like Segment Anything Model (SAM) to enhance performance. These advancements have significant implications for various applications, including medical image analysis (e.g., lymph node detection, lung nodule segmentation), remote sensing (e.g., slum detection), and video object tracking, where precise segmentation is critical for accurate diagnosis, monitoring, and analysis.