Chart Question Answering
3개 벤치마크 · 논문 65편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Papers
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate visual grounding and coherent reasoning…
Chart Question AnsweringMultimodal ReasoningLogical ReasoningVisual ReasoningEnhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: standard cross-entropy treats numeric toke…
Chart Question AnsweringMathematical ReasoningAgentFinVQA: A Deployable Multi-Agent Pipeline for Auditable Financial Chart QA
Financial chart question answering in regulated settings demands more than accuracy: practitioners must know which answers to trust before acting on them, and many institutions cannot send client data to external model p…
Chart Question AnsweringSAFE-Cascade: Cost-Adaptive Vision-Language Routing for Chart Question Answering
Vision-language models (VLMs) are powerful for chart question answering, but invoking a VLM for every query can be unnecessarily expensive when many questions are answerable from OCR text and lightweight language reasoni…
Chart Question AnsweringVisual GroundingHierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning
Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While existing MLLMs are strong at understanding single plots, they often stru…
Chart Question AnsweringChart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern recognition, linguistic reasoning must integrate with v…
parameter-efficient fine-tuningChart Question AnsweringReinforcement LearningVisual Reasoning