4 papers
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Shumeng Li, Jian Zhang, Lei Qi +3
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled da…
AWF: Adaptive Weight Fusion for Enhanced Class Incremental Semantic Segmentation
Zechao Sun, Shuying Piao, Haolin Jin +4
Class Incremental Semantic Segmentation (CISS) aims to mitigate catastrophic forgetting by maintaining a balance between previously learned and newly introduced knowledge. Existing…
Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation
Zihan Cheng, Jintao Guo, Jian Zhang +4
To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen ta…
TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances
Wenting Xu, Viorela Ila, Luping Zhou +1
The concept of function and affordance is a critical aspect of 3D scene understanding and supports task-oriented objectives. In this work, we develop a model that learns to structu…