7 citations · 7 across the 4 of their papers we have counts for
4 papers
In-Context Semi-Supervised Learning
Jiashuo Fan, Paul Rosu, Aaron T. Wang +3
There has been significant recent interest in understanding the capacity of Transformers for in-context learning (ICL), yet most theory focuses on supervised settings with explicit…
Model Reprogramming Demystified: A Neural Tangent Kernel Perspective
Ming-Yu Chung, Jiashuo Fan, Hancheng Ye +5
Model Reprogramming (MR) is a resource-efficient framework that adapts large pre-trained models to new tasks with minimal additional parameters and data, offering a promising solut…
ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations
Leyao Liu, Tao Kong, Minzhao Zhu +2
3D instance segmentation methods often require fully-annotated dense labels for training, which are costly to obtain. In this paper, we present ClickSeg, a novel click-level weakly…
UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation
Jiashuo Fan, Bin Gao, Huan Jin +1
Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framewo…