4 papers
At Which Training Stage Does Code Data Help LLMs Reasoning?
Yingwei Ma, Yue Liu, Yue Yu +4
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities and become the foundation of language technologies. Inspired by the great success of code data in trai…
Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration
Zheng Qin, Hao Yu, Changjian Wang +2
We study the problem of outlier correspondence pruning for non-rigid point cloud registration. In rigid registration, spatial consistency has been a commonly used criterion to disc…
Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion
Honghui Liu, Changjian Wang, Kele Xu +4
Wound image segmentation is a critical component for the clinical diagnosis and in-time treatment of wounds. Recently, deep learning has become the mainstream methodology for wound…
Trusted Multi-Scale Classification Framework for Whole Slide Image
Ming Feng, Kele Xu, Nanhui Wu +4
Despite remarkable efforts been made, the classification of gigapixels whole-slide image (WSI) is severely restrained from either the constrained computing resources for the whole…