Hi, we'd like to add a related a paper to the paper list, GITA: Graph to Image-Text Integration for Vision-Language Graph Reasoning, which was accepted by NeurIPS 2024.
Paper.
GITA is the first work to explore and establish the promising vision-language question answering on graph-related reasoning area. It systematically enable VLMs for general language-based graph reasoning tasks.
In this paper, we provide new pre-trained VLM model weights for graph reasoning:
Model: GITA-7B/13B, the model weights are in both Github repo and Model weight huggingface.
We also proposed the first dataset GVLQA for vision-language graph reasoning, they are VQA image-text-query-answer pairs for graph reasoning. GVLQA Datasets.
Wish your research smoothly. Looking forward to your reply!
Hi, we'd like to add a related a paper to the paper list, GITA: Graph to Image-Text Integration for Vision-Language Graph Reasoning, which was accepted by NeurIPS 2024.
Paper.
GITA is the first work to explore and establish the promising vision-language question answering on graph-related reasoning area. It systematically enable VLMs for general language-based graph reasoning tasks.
In this paper, we provide new pre-trained VLM model weights for graph reasoning:
Model: GITA-7B/13B, the model weights are in both Github repo and Model weight huggingface.
We also proposed the first dataset GVLQA for vision-language graph reasoning, they are VQA image-text-query-answer pairs for graph reasoning. GVLQA Datasets.
Wish your research smoothly. Looking forward to your reply!