5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
DMTG: One-Shot Differentiable Multi-Task Grouping
Yuan Gao, Shuguo Jiang, Moran Li +2
We aim to address Multi-Task Learning (MTL) with a large number of tasks by Multi-Task Grouping (MTG). Given N tasks, we propose to simultaneously identify the best task groups fro…
cs.CV2024
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
Yu Ming, Zihao Wu, Jie Yang +7
Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this…
cs.CV2024★ 5 cited
Complete Instances Mining for Weakly Supervised Instance Segmentation
Zecheng Li, Zening Zeng, Yuqi Liang +1
Weakly supervised instance segmentation (WSIS) using only image-level labels is a challenging task due to the difficulty of aligning coarse annotations with the finer task. However…