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Chart Understanding

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Evanwu1125/ChartCards

공식 코드

ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding

https://github.com/MuyeHuang/EvoChart

공식 코드

EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding

https://github.com/Ucas-HaoranWei/Vary

공식 코드

Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

https://github.com/chartgalaxy/chartgalaxy

공식 코드

ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation

https://github.com/cooldawnant/infochartqa

공식 코드

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts

https://github.com/deepseek-ai/deepseek-vl2

공식 코드

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

https://github.com/fuxiaoliu/mmc

공식 코드

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

https://github.com/hkust-nlp/vision4chart

공식 코드

On the Perception Bottleneck of VLMs for Chart Understanding

https://github.com/khuangaf/awesome-chart-understanding

공식 코드

From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models

https://github.com/kushalkafle/DVQA_dataset

공식 코드

DVQA: Understanding Data Visualizations via Question Answering

https://github.com/moured/refchartqa

공식 코드

RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning

https://github.com/muyehuang/chartsketcher

공식 코드

ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding

https://github.com/omron-sinicx/sbsfigures

공식 코드

SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images

https://github.com/orionbench/orionbench

공식 코드

OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics

https://github.com/princeton-nlp/CharXiv

공식 코드

CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

https://github.com/princeton-nlp/ptp

공식 코드

Improving Language Understanding from Screenshots

https://github.com/sootung/askchart

공식 코드

AskChart: Universal Chart Understanding through Textual Enhancement

https://github.com/thunlp/ChartCoder

공식 코드

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

https://github.com/unimodal4reasoning/chartvlm

공식 코드

StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding

https://github.com/unimodal4reasoning/simchart9k

공식 코드

StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding

https://github.com/vis-nlp/chartgemma

공식 코드

ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild

https://github.com/vis-nlp/chartinstruct

공식 코드

ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning

https://github.com/vis-nlp/chartqapro

공식 코드

ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering

https://github.com/vis-nlp/unichart

공식 코드

UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning

https://github.com/x-plug/mplug-docowl

공식 코드

TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning

https://github.com/xxlllz/chartedit

공식 코드

ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing

https://github.com/zhiqic/chartreader

공식 코드

ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules

https://github.com/zwq2018/multi-modal-self-instruct

공식 코드

Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model

https://github.com/FuxiaoLiu/LRV-Instruction

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

https://github.com/FuxiaoLiu/VisualNews-Repository

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

https://github.com/alpha-innovator/chartvlm

StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding

https://github.com/tianyi-lab/hallusionbench

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

https://github.com/yangyucheng000/Paper-4/tree/main/mm_MindSpore

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

https://github.com/yangyucheng000/papercode-2/tree/main/mm_MindSpore-main

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.