Papers Chart Understanding
“Chart Understanding” 태그가 달린 논문 47편 · 필터 해제
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promising performance by focusing on visual feat…
Chart Question AnsweringChart UnderstandingInstruction FollowingOptical Character Recognition (OCR)+1InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
Understanding infographic charts with design-driven visual elements (e.g., pictograms, icons) requires both visual recognition and reasoning, posing challenges for multimodal large language models (MLLMs). However, exist…
Chart UnderstandingQuestion AnsweringVisual Question AnsweringChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding
Charts are high-density visualization carriers for complex data, serving as a crucial medium for information extraction and analysis. Automated chart understanding poses significant challenges to existing multimodal larg…
Chart UnderstandingLogical Reasoningmultimodal interactionVisual ReasoningChartLens: Fine-grained Visual Attribution in Charts
The growing capabilities of multimodal large language models (MLLMs) have advanced tasks like chart understanding. However, these models often suffer from hallucinations, where generated text sequences conflict with the …
Chart UnderstandingChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
Infographic charts are a powerful medium for communicating abstract data by combining visual elements (e.g., charts, images) with textual information. However, their visual and structural richness poses challenges for la…
BenchmarkingChart UnderstandingCode GenerationMultimodal ReasoningOrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics
Given the central role of charts in scientific, business, and communication contexts, enhancing the chart understanding capabilities of vision-language models (VLMs) has become increasingly critical. A key limitation of …
Chart Understandingobject-detectionObject DetectionVisual GroundingChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
The emergence of Multi-modal Large Language Models (MLLMs) presents new opportunities for chart understanding. However, due to the fine-grained nature of these tasks, applying MLLMs typically requires large, high-quality…
Chart Question AnsweringChart UnderstandingQuestion AnsweringRetrievalChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Chart understanding presents a unique challenge for large vision-language models (LVLMs), as it requires the integration of sophisticated textual and visual reasoning capabilities. However, current LVLMs exhibit a notabl…
Chart Question AnsweringChart UnderstandingQuestion AnsweringVisual ReasoningChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
Although multimodal large language models (MLLMs) show promise in generating chart rendering code, chart editing presents a greater challenge. This difficulty stems from its nature as a labor-intensive task for humans th…
Chart UnderstandingChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
Charts are ubiquitous, as people often use them to analyze data, answer questions, and discover critical insights. However, performing complex analytical tasks with charts requires significant perceptual and cognitive ef…
Chart Question AnsweringChart UnderstandingMultiple-choiceQuestion AnsweringRefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
Recently, Vision Language Models (VLMs) have increasingly emphasized document visual grounding to achieve better human-computer interaction, accessibility, and detailed understanding. However, its application to visualiz…
Chart Question AnsweringChart UnderstandingQuestion AnsweringVisual GroundingOn the Perception Bottleneck of VLMs for Chart Understanding
Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large visi…
Chart UnderstandingContrastive LearningWhy Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
Vision Language Models (VLMs) have achieved remarkable progress in multimodal tasks, yet they often struggle with visual arithmetic, seemingly simple capabilities like object counting or length comparison, which are esse…
Arithmetic ReasoningChart UnderstandingDecoderMath+1ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks. However, interpreting charts with textual descriptions often leads to information loss, as it fails to full…
Chart UnderstandingCode GenerationLanguage ModelingLanguage Modelling+2ChartAdapter: Large Vision-Language Model for Chart Summarization
Chart summarization, which focuses on extracting key information from charts and interpreting it in natural language, is crucial for generating and delivering insights through effective and accessible data analysis. Trad…
Chart Understandingcross-modal alignmentLanguage ModelingLanguage Modelling+1AskChart: Universal Chart Understanding through Textual Enhancement
Chart understanding tasks such as ChartQA and Chart-to-Text involve automatically extracting and interpreting key information from charts, enabling users to query or convert visual data into structured formats. State-of-…
Chart UnderstandingMixture-of-ExpertsSBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and generating QAs. Although recent developme…
Chart Question AnsweringChart UnderstandingDeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component…
Chart UnderstandingMixture-of-ExpertsOptical Character RecognitionQuestion Answering+3Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends
In recent years, interest in vision-language tasks has grown, especially those involving chart interactions. These tasks are inherently multimodal, requiring models to process chart images, accompanying text, underlying …
BenchmarkingChart UnderstandingOptical Character Recognition (OCR)Prompt Engineering+1SynChart: Synthesizing Charts from Language Models
With the release of GPT-4V(O), its use in generating pseudo labels for multi-modality tasks has gained significant popularity. However, it is still a secret how to build such advanced models from its base large language …
Chart Understanding