3 papers
cs.CV2025
Enhancing Scientific Visual Question Answering via Vision-Caption aware Supervised Fine-Tuning
Janak Kapuriya, Anwar Shaikh, Arnav Goel +8
In this study, we introduce Vision-Caption aware Supervised FineTuning (VCASFT), a novel learning paradigm designed to enhance the performance of smaller Vision Language Models(VLM…
cs.LG2020
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning
Yejiang Wang, Yuhai Zhao, Zhengkui Wang +1
Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learning framework, which aims to learn a multi-label classifier from a set of labeled bags each containing a numbe…
cs.DB2017
VCExplorer: A Interactive Graph Exploration Framework Based on Hub Vertices with Graph Consolidation
Huiju Wang, Zhengkui Wang, Kian-Lee Tan +3
Graphs have been widely used to model different information networks, such as the Web, biological networks and social networks (e.g. Twitter). Due to the size and complexity of the…