Cover Image

Diagnosing Bottlenecks in Data Visualization Understanding by Vision-Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tartaglini, Alexa R., Grant, Satchel, Wurgaft, Daniel, Potts, Christopher, Fan, Judith E.
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models
by: Verma, Arnav, et al.
Published: (2025)

I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers
by: Tartaglini, Alexa R., et al.
Published: (2026)

Do Vision-Language Models Really Understand Visual Language?
by: Hou, Yifan, et al.
Published: (2024)

Do Vision-Language Models Understand Visual Persuasiveness?
by: Park, Gyuwon
Published: (2025)

Diagnosing Vision Language Models' Perception by Leveraging Human Methods for Color Vision Deficiencies
by: Hayashi, Kazuki, et al.
Published: (2025)

Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
by: Zhu, Yingjie, et al.
Published: (2024)

On the Perception Bottleneck of VLMs for Chart Understanding
by: Liu, Junteng, et al.
Published: (2025)

CommVQA: Situating Visual Question Answering in Communicative Contexts
by: Naik, Nandita Shankar, et al.
Published: (2024)

Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
by: Ding, Yi, et al.
Published: (2025)

Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding
by: Luo, Chuwei, et al.
Published: (2022)

Vision-Language Models Do Not Understand Negation
by: Alhamoud, Kumail, et al.
Published: (2025)

Do Vision-Language Models Understand Compound Nouns?
by: Kumar, Sonal, et al.
Published: (2024)

On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning
by: Kim, Geewook, et al.
Published: (2024)

Vision-Language Modeling in PET/CT for Visual Grounding of Positive Findings
by: Huemann, Zachary, et al.
Published: (2025)

Visual In-Context Learning for Large Vision-Language Models
by: Zhou, Yucheng, et al.
Published: (2024)

Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering
by: Guo, Danfeng, et al.
Published: (2024)

PUMGPT: A Large Vision-Language Model for Product Understanding
by: Xue, Wei, et al.
Published: (2023)

Toward Interactive Regional Understanding in Vision-Large Language Models
by: Lee, Jungbeom, et al.
Published: (2024)

Can Large Vision-Language Models Understand Multimodal Sarcasm?
by: Wang, Xinyu, et al.
Published: (2025)

Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding
by: Chung, Jiwan, et al.
Published: (2024)

BabyVision: Visual Reasoning Beyond Language
by: Chen, Liang, et al.
Published: (2026)

Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
by: Atuhurra, Jesse, et al.
Published: (2024)

Large Vision-Language Models for Remote Sensing Visual Question Answering
by: Siripong, Surasakdi, et al.
Published: (2024)

Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions
by: Jian, Pu, et al.
Published: (2025)

VividMed: Vision Language Model with Versatile Visual Grounding for Medicine
by: Luo, Lingxiao, et al.
Published: (2024)

Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
by: Ye, Jiacheng, et al.
Published: (2025)

Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
by: Huang, Kung-Hsiang, et al.
Published: (2025)

The Visual Iconicity Challenge: Evaluating Vision-Language Models on Sign Language Form-Meaning Mapping
by: Keleş, Onur, et al.
Published: (2025)

Understanding Museum Exhibits using Vision-Language Reasoning
by: Balauca, Ada-Astrid, et al.
Published: (2024)

Refining Skewed Perceptions in Vision-Language Contrastive Models through Visual Representations
by: Dai, Haocheng, et al.
Published: (2024)

Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models
by: Jian, Pu, et al.
Published: (2025)

A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
by: Jing, Liqiang, et al.
Published: (2025)

Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models
by: Li, Bin, et al.
Published: (2025)

Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
by: Hu, Yushi, et al.
Published: (2023)

Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering
by: Ha, Cuong Nhat, et al.
Published: (2024)

Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models
by: Zhou, Yucheng, et al.
Published: (2024)

VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models
by: Zhang, Ce, et al.
Published: (2025)

ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
by: Tang, Liyan, et al.
Published: (2025)

HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
by: Wei, Yanbin, et al.
Published: (2026)

Rethinking the Mixture of Vision Encoders Paradigm for Enhanced Visual Understanding in Multimodal LLMs
by: Azadani, Mozhgan Nasr, et al.
Published: (2025)