1 code implementation • 27 Feb 2024 • Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin, Yanye Lu
In this study, we propose a class-driven scribble promotion network, which utilizes both scribble annotations and pseudo-labels informed by image-level classes and global semantics for supervision.
1 code implementation • 23 Feb 2024 • Yihao Zhang, Hangzhou He, Jingyu Zhu, Huanran Chen, Yifei Wang, Zeming Wei
Instead of perturbing the samples, Sharpness-Aware Minimization (SAM) perturbs the model weights during training to find a more flat loss landscape and improve generalization.
no code implementations • 9 Aug 2023 • Lei Zhu, Hangzhou He, Xinliang Zhang, Qian Chen, Shuang Zeng, Qiushi Ren, Yanye Lu
Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch.