https://github.com/Evanwu1125/ChartCards
공식 코드ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MuyeHuang/VProChart
공식 코드https://github.com/adlnlp/msg-chart
공식 코드https://github.com/anranwu-richpo/dcqa
공식 코드https://github.com/cse-ai-lab/RealCQA
공식 코드RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic
https://github.com/google-research-datasets/screen_annotation
공식 코드ScreenAI: A Vision-Language Model for UI and Infographics Understanding
https://github.com/google-research-datasets/screen_qa
공식 코드ScreenAI: A Vision-Language Model for UI and Infographics Understanding
https://github.com/google-research/pix2struct
공식 코드Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/kushalkafle/DVQA_dataset
공식 코드DVQA: Understanding Data Visualizations via Question Answering
https://github.com/kushalkafle/PREFIL
공식 코드Answering Questions about Data Visualizations using Efficient Bimodal Fusion
https://github.com/levymsn/cqa-crct
공식 코드https://github.com/moured/refchartqa
공식 코드RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
https://github.com/omron-sinicx/sbsfigures
공식 코드SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
https://github.com/qwenlm/qwen-vl
공식 코드Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/sangwu99/simplot
공식 코드SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
https://github.com/tahmedge/chart_lvlm_judge
공식 코드https://github.com/unimodal4reasoning/chartvlm
공식 코드StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
https://github.com/unimodal4reasoning/simchart9k
공식 코드StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
https://github.com/vis-nlp/chartqa
공식 코드ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
https://github.com/vis-nlp/chartqapro
공식 코드ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
https://github.com/vis-nlp/unichart
공식 코드UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
https://github.com/vmichals/FigureQA-baseline
공식 코드https://github.com/zengxingchen/chartqa-mllm
공식 코드https://github.com/MindCode-4/code-2/tree/main/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/alpha-innovator/chartvlm
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
https://github.com/brandon3964/multimodal-task-vector
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/doc-doc/NExT-OE
PaLI-X: On Scaling up a Multilingual Vision and Language Model
https://github.com/huggingface/transformers
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/kyegomez/PALI
PaLI-X: On Scaling up a Multilingual Vision and Language Model
https://github.com/kyegomez/PALI3
https://github.com/valdecy/pybibx
https://github.com/yangyucheng000/University/tree/main/model-1/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.