Target Mask

Target masks, representing the precise boundaries of objects within images or other data, are crucial for various computer vision tasks like segmentation and object detection. Current research focuses on improving mask generation and refinement through techniques like diffusion models, graph neural networks, and transformer architectures, often addressing challenges related to data scarcity, noisy inputs, and computational efficiency. These advancements are significant for improving the accuracy and reliability of automated image analysis across diverse applications, including medical imaging, autonomous driving, and remote sensing.