22 citations · 31 across the 21 of their papers we have counts for
26 papers
CVIF: A Criticality-Driven Visual Intervention Framework for Geometric Diagram Understanding in MLLMs
Jiahui Kang, Bifan Wei, Lingling Zhang +4
Despite significant progress in visual tasks by Multimodal Large Language Models (MLLMs), geometric diagram understanding remains challenging due to the presence of sparse visual c…
DuplexOmni: Real-Time Listening, Seeing, Thinking, and Speaking for Full-Duplex Interaction
Muye Huang, Lingling Zhang, Xingyu Yu +7
Human interaction is continuous, multimodal, and full-duplex by nature. Although recent omni models have made substantial progress in unified speech, vision, and text modeling, com…
ChartAct: A Benchmark for Dynamic Chart Understanding
Muye Huang, Lin Wu, Lingling Zhang +5
Charts are widely used to present complex data for analysis and decision making. Existing chart understanding benchmarks mainly focus on static charts, but real-world charts are of…
ReDiffuse: Rotation Equivariant Diffusion Model for Multi-focus Image Fusion
Bo Li, Tingting Bao, Lingling Zhang +3
Diffusion models have achieved impressive performance on multi-focus image fusion (MFIF). However, a key challenge in applying diffusion models to the ill-posed MFIF problem is tha…
ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics
Weiping Fu, Bifan Wei, Jingyi Hao +7
Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, includi…
MAXS: Meta-Adaptive Exploration with LLM Agents
Jian Zhang, Zhiyuan Wang, Zhangqi Wang +7
Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools. However, during agent inference, existing methods often suffer f…
SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and More
Muye Huang, Lingling Zhang, Yifei Li +2
Charts are high-density visual carriers of complex data and medium for information extraction and analysis. Due to the need for precise and complex visual reasoning, automated char…
CoFFT: Chain of Foresight-Focus Thought for Visual Language Models
Xinyu Zhang, Yuxuan Dong, Lingling Zhang +5
Despite significant advances in Vision Language Models (VLMs), they remain constrained by the complexity and redundancy of visual input. When images contain large amounts of irrele…
Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs
Shihao Qi, Jie Ma, Ziang Yin +5
Existing methods usually leverage a fixed strategy, such as natural language reasoning, code-augmented reasoning, tool-integrated reasoning, or ensemble-based reasoning, to guide L…
Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion
Bo Li, Yunkuo Lei, Tingting Bao +5
Multi-focus image fusion (MFIF) is a crucial technique in image processing, with a key challenge being the generation of decision maps with precise boundaries. However, traditional…
SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Yifei Li, Lingling Zhang, Hang Yan +4
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs…
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…
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning
Xinyu Zhang, Yuxuan Dong, Yanrui Wu +6
Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based re…
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…
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin
Tianlin Guo, Lingling Zhang, Jiaxin Wang +4
Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than t…
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…
Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text
Fangzhi Xu, Jun Liu, Qika Lin +3
Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of opti…
GPTR: Gestalt-Perception Transformer for Diagram Object Detection
Xin Hu, Lingling Zhang, Jun Liu +3
Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its v…
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Fangzhi Xu, Jun Liu, Qika Lin +2
Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability…
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering
Fangzhi Xu, Qika Lin, Jun Liu +4
Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering (VQA…
Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning
Yudai Pan, Jun Liu, Lingling Zhang +3
Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is learning the embeddings of relations and e…