These are the official implementation resources, model weights, and prediction files for ChartLens, the champion solution for DataMFM Challenge Track 2: Chart Understanding at CVPR 2026.

🔗 Paper: ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement
🔗 GitHub Repository: iLearnLab/CVPRW26-ChartLens
🔗 Challenge Page: DataMFM Challenge

📌 Model Information

1. Model Name

ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement

2. Task Type & Applicable Tasks

Task Type: Chart Understanding / Multimodal Document Understanding
Applicable Tasks: Chart-to-CSV extraction and chart-to-summary generation from chart images.

3. Project Introduction

Chart understanding requires models to recover structured chart data and generate faithful natural-language summaries from chart images. ChartLens addresses these complementary goals with a dual-branch, verification-guided correction framework.

💡 Method Highlight: ChartLens combines Granite-Vision-4.1-4B LoRA adaptation with two correction branches: Structure-Aware CSV Verification and Correction (SAVC) for reliable table recovery, and Text-Retention-Guided Summary Refinement (TRSR) for OCR-assisted factual summary repair. SAVC checks structure, completeness, and numerical accuracy, while TRSR preserves visible chart text such as titles, legends, annotations, sources, and numerical evidence.

4. Training Data Source

Released ChartNet-based training data for LoRA adaptation.
DataMFM Challenge chart understanding splits, including real and synthetic chart images.

5. Challenge Results

Method	CSV Numeric F1	CSV Structural Score	Summary ROUGE-L	Summary Numeric Fact F1	Overall
ChartLens (Ours)	80.62	75.66	45.57	74.55	69.10

ChartLens ranked 1st place on DataMFM Challenge Track 2.

🚀 Usage & Basic Inference

Step 1: Prepare the Environment

Clone the GitHub repository and set up the Conda environment:

bash

git clone https://github.com/iLearnLab/CVPRW26-ChartLens.git
cd CVPRW26-ChartLens

bash

conda create -n chartlens python=3.10 -y
conda activate chartlens
pip install -r requirements.txt

Step 2: Data & Weights Preparation

Challenge Data: Use the datasets and splits released by the DataMFM Challenge. The chart understanding track contains real and synthetic splits.
ChartLens Checkpoints: Download the model weights from this Hugging Face repository.
Granite Vision Backbone: Prepare the Granite-Vision-4.1-4B backbone and update the local --model_path argument when running inference.

To prepare ChartNet SFT data for LoRA training:

bash

python code/load_chartnet_500.py \
  --out_dir Fine-tuning/Dataset/raw \
  --num_samples 500

python code/build_chartnet_sft.py \
  --gt_path Fine-tuning/Dataset/raw/gt.jsonl \
  --image_dir Fine-tuning/Dataset/raw/images \
  --out_dir Fine-tuning/Dataset/sft \
  --csv_repeat 2 \
  --summary_repeat 1

Step 3: Run Granite Vision + LoRA Inference

bash

python code/infer_granite_with_lora.py \
  --image_root /path/to/data \
  --out_root /path/to/output \
  --model_path /path/to/granite-vision-4.1-4b \
  --lora_path /path/to/chartlens_lora \
  --gpu_id 0 \
  --splits real synthetic

Use code/infer_chartnet_granite.py for base Granite Vision inference without a LoRA adapter.

📝⭐️ Citation

If you find this project useful for your research, please consider citing:

bibtex

@article{liu2026chartlens,
  title={ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement},
  author={Liu, Hao and Cao, Ruping and Wang, Kun and Li, Zhiran and Liu, Fan and Hu, Yupeng and Nie, Liqiang},
  journal={arXiv preprint arXiv:2606.10640},
  year={2026}
}

iLearn-Lab/CVPRW26-ChartLens

🚀 ChartLens @ CVPR 2026 DataMFM Chart Understanding Challenge

📌 Model Information

1. Model Name

2. Task Type & Applicable Tasks

3. Project Introduction

4. Training Data Source

5. Challenge Results

🚀 Usage & Basic Inference

Step 1: Prepare the Environment

Step 2: Data & Weights Preparation

Step 3: Run Granite Vision + LoRA Inference

📝⭐️ Citation

iLearn-Lab/
CVPRW26-ChartLens