On the Perception Bottleneck of VLMs for Chart Understanding
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Junteng, Zeng, Weihao, Zhang, Xiwen, Wang, Yijun, Shan, Zifei, He, Junxian |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
by: Wang, Zirui, et al.
Published: (2024)
by: Wang, Zirui, et al.
Published: (2024)
Understanding and Rectifying Safety Perception Distortion in VLMs
by: Zou, Xiaohan, et al.
Published: (2025)
by: Zou, Xiaohan, et al.
Published: (2025)
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
by: Li, Zhen, et al.
Published: (2025)
by: Li, Zhen, et al.
Published: (2025)
CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
by: Deng, Dazhen, et al.
Published: (2025)
by: Deng, Dazhen, et al.
Published: (2025)
CIVET: Systematic Evaluation of Understanding in VLMs
by: Rizzoli, Massimo, et al.
Published: (2025)
by: Rizzoli, Massimo, et al.
Published: (2025)
ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing
by: Li, Shuo, et al.
Published: (2026)
by: Li, Shuo, et al.
Published: (2026)
Effective Training Data Synthesis for Improving MLLM Chart Understanding
by: Yang, Yuwei, et al.
Published: (2025)
by: Yang, Yuwei, et al.
Published: (2025)
ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness
by: Liang, Yijun, et al.
Published: (2025)
by: Liang, Yijun, et al.
Published: (2025)
Diving into Self-Evolving Training for Multimodal Reasoning
by: Liu, Wei, et al.
Published: (2024)
by: Liu, Wei, et al.
Published: (2024)
SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs
by: Avogaro, Niccolo, et al.
Published: (2026)
by: Avogaro, Niccolo, et al.
Published: (2026)
Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding
by: Ye, Junyi, et al.
Published: (2024)
by: Ye, Junyi, et al.
Published: (2024)
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
by: Liu, Peng, et al.
Published: (2025)
by: Liu, Peng, et al.
Published: (2025)
SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking
by: Li, Sifan, et al.
Published: (2025)
by: Li, Sifan, et al.
Published: (2025)
MSG-Chart: Multimodal Scene Graph for ChartQA
by: Dai, Yue, et al.
Published: (2024)
by: Dai, Yue, et al.
Published: (2024)
VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images
by: Zhou, Guanyu, et al.
Published: (2026)
by: Zhou, Guanyu, et al.
Published: (2026)
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better
by: Wang, Dianyi, et al.
Published: (2025)
by: Wang, Dianyi, et al.
Published: (2025)
Tone Matters: The Impact of Linguistic Tone on Hallucination in VLMs
by: Hong, Weihao, et al.
Published: (2026)
by: Hong, Weihao, et al.
Published: (2026)
Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs
by: Shen, Yifan, et al.
Published: (2025)
by: Shen, Yifan, et al.
Published: (2025)
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
by: Kondic, Jovana, et al.
Published: (2026)
by: Kondic, Jovana, et al.
Published: (2026)
Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness
by: Mukhopadhyay, Srija, et al.
Published: (2024)
by: Mukhopadhyay, Srija, et al.
Published: (2024)
ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
by: Xu, Zhengzhuo, et al.
Published: (2024)
by: Xu, Zhengzhuo, et al.
Published: (2024)
ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
by: Wang, Xingqi, et al.
Published: (2025)
by: Wang, Xingqi, et al.
Published: (2025)
MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
by: Zhu, Zifeng, et al.
Published: (2024)
by: Zhu, Zifeng, et al.
Published: (2024)
Are VLMs Really Blind
by: Singh, Ayush, et al.
Published: (2024)
by: Singh, Ayush, et al.
Published: (2024)
CHAOS: Chart Analysis with Outlier Samples
by: Moured, Omar, et al.
Published: (2025)
by: Moured, Omar, et al.
Published: (2025)
ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement
by: Salamatian, Ali, et al.
Published: (2025)
by: Salamatian, Ali, et al.
Published: (2025)
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
by: Qiao, Yuxuan, et al.
Published: (2024)
by: Qiao, Yuxuan, et al.
Published: (2024)
On Pre-training of Multimodal Language Models Customized for Chart Understanding
by: Fan, Wan-Cyuan, et al.
Published: (2024)
by: Fan, Wan-Cyuan, et al.
Published: (2024)
Reasmory: 3D Reconstruction as Explicit Memory for VLMs Spatial Reasoning
by: He, Jixuan, et al.
Published: (2026)
by: He, Jixuan, et al.
Published: (2026)
CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation
by: Wang, Yuxuan, et al.
Published: (2024)
by: Wang, Yuxuan, et al.
Published: (2024)
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
by: Akhtar, Mubashara, et al.
Published: (2023)
by: Akhtar, Mubashara, et al.
Published: (2023)
[De|Re]constructing VLMs' Reasoning in Counting
by: Alghisi, Simone, et al.
Published: (2025)
by: Alghisi, Simone, et al.
Published: (2025)
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations
by: Ford, James, et al.
Published: (2024)
by: Ford, James, et al.
Published: (2024)
Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning
by: Dong, Qihua, et al.
Published: (2026)
by: Dong, Qihua, et al.
Published: (2026)
Diagnosing Bottlenecks in Data Visualization Understanding by Vision-Language Models
by: Tartaglini, Alexa R., et al.
Published: (2025)
by: Tartaglini, Alexa R., et al.
Published: (2025)
TechING: Towards Real World Technical Image Understanding via VLMs
by: Nadeem, Tafazzul, et al.
Published: (2026)
by: Nadeem, Tafazzul, et al.
Published: (2026)
In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding
by: Fan, Wan-Cyuan, et al.
Published: (2025)
by: Fan, Wan-Cyuan, et al.
Published: (2025)
Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
by: Wang, Zhaokai, et al.
Published: (2025)
by: Wang, Zhaokai, et al.
Published: (2025)
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
by: Yang, Cheng, et al.
Published: (2024)
by: Yang, Cheng, et al.
Published: (2024)
Visual Confused Deputy: Exploiting and Defending Perception Failures in Computer-Using Agents
by: Liu, Xunzhuo, et al.
Published: (2026)
by: Liu, Xunzhuo, et al.
Published: (2026)
Similar Items
-
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
by: Wang, Zirui, et al.
Published: (2024) -
Understanding and Rectifying Safety Perception Distortion in VLMs
by: Zou, Xiaohan, et al.
Published: (2025) -
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
by: Li, Zhen, et al.
Published: (2025) -
CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
by: Deng, Dazhen, et al.
Published: (2025) -
CIVET: Systematic Evaluation of Understanding in VLMs
by: Rizzoli, Massimo, et al.
Published: (2025)