Papers with Code 아카이브
Chart Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 3
- 보존된 평가 기록
- 38
Question Answering task on charts images
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
DomainCQA: Crafting Expert-Level QA from Domain-Specific Charts
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
RealCQA-V2 : Visual Premise Proving A Manual COT Dataset for Charts
ChartKG: A Knowledge-Graph-Based Representation for Chart Images
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations
GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering
VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning
MSG-Chart: Multimodal Scene Graph for ChartQA
Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning
Advancing Chart Question Answering with Robust Chart Component Recognition
Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs
ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs
SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Gemini: A Family of Highly Capable Multimodal Models
Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
PaLI-3 Vision Language Models: Smaller, Faster, Stronger
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic
PaLI-X: On Scaling up a Multilingual Vision and Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.