Segmentation Model
Segmentation models aim to partition images into meaningful regions, a crucial task across diverse fields like medical imaging and autonomous driving. Current research emphasizes improving model robustness and efficiency, focusing on architectures like U-Nets, Transformers, and diffusion models, often incorporating techniques like continual learning and prompt engineering to adapt to new data or tasks with minimal retraining. These advancements are driving improvements in accuracy and reducing the need for extensive labeled datasets, leading to wider applicability in various scientific and industrial applications.
Papers
Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network
Fahim Ahmed Zaman, Wahidul Alam, Tarun Kanti Roy, Amanda Chang, Kan Liu, Xiaodong Wu
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
Mengyu Wang, Henghui Ding, Jun Hao Liew, Jiajun Liu, Yao Zhao, Yunchao Wei