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Chart Understanding
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding
ChartLens: Fine-grained Visual Attribution in Charts
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics
ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
On the Perception Bottleneck of VLMs for Chart Understanding
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
ChartAdapter: Large Vision-Language Model for Chart Summarization
AskChart: Universal Chart Understanding through Textual Enhancement
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends
SynChart: Synthesizing Charts from Language Models
C2F-CHART: A Curriculum Learning Approach to Chart Classification
ChartMoE: Mixture of Expert Connector for Advanced Chart Understanding
EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding
ChartEye: A Deep Learning Framework for Chart Information Extraction
Revisiting Multi-Modal LLM Evaluation
Assessing Graphical Perception of Image Embedding Models using Channel Effectiveness
On Pre-training of Multimodal Language Models Customized for Chart Understanding
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
ReMI: A Dataset for Reasoning with Multiple Images
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs
TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Improving Language Understanding from Screenshots
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