7 papers
GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines
Yumeng Fu, Jiayin Zhu, Lingling Zhang +5
Geometry problem solving (GPS) poses significant challenges for Multimodal Large Language Models (MLLMs) in diagram comprehension, knowledge application, long-step reasoning, and a…
ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding
Muye Huang, Lingling Zhang, Jie Ma +6
Charts are high-density visualization carriers for complex data, serving as a crucial medium for information extraction and analysis. Automated chart understanding poses significan…
Diagram-Driven Course Questions Generation
Xinyu Zhang, Lingling Zhang, Yanrui Wu +6
Visual Question Generation (VQG) research focuses predominantly on natural images while neglecting the diagram, which is a critical component in educational materials. To meet the…
GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering
Lingling Zhang, Muye Huang, QianYing Wang +3
Chart Question Answering (CQA) aims at answering questions based on the visual chart content, which plays an important role in chart sumarization, business data analysis, and data…
VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning
Muye Huang, Lingling Zhang, Lai Han +3
Charts are widely used for data visualization across various fields, including education, research, and business. Chart Question Answering (CQA) is an emerging task focused on the…
EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding
Muye Huang, Han Lai, Xinyu Zhang +4
Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs)…
Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery
Jiaxin Wang, Lingling Zhang, Jun Liu +2
We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relatio…