Enhance dataset card: Add paper/project/code links, abstract, paper citation, and update task categories (#1)

- Enhance dataset card: Add paper/project/code links, abstract, paper citation, and update task categories (fc21fdb8bb13d0d5b39337a01220b36f7af1cb0c)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,11 +1,12 @@
 ---
-pretty_name: GRAID Waymo Perception Dataset Question-Answer Dataset
 language:
 - en
 license: cc-by-nc-4.0
 task_categories:
 - visual-question-answering
 - object-detection
+- image-text-to-text
+pretty_name: GRAID Waymo Perception Dataset Question-Answer Dataset
 tags:
 - visual-reasoning
 - spatial-reasoning
@@ -61,12 +62,17 @@ configs:
 
 # GRAID Waymo Perception Dataset Question-Answer Dataset
 
+[Paper](https://huggingface.co/papers/2510.22118) | [Project Page](https://ke7.github.io/graid/) | [Code](https://github.com/kd7-ml/graid)
+
 ## Overview
 
 This dataset was generated using **GRAID** (**G**enerating **R**easoning questions from **A**nalysis of **I**mages via **D**iscriminative artificial intelligence), a framework for creating spatial reasoning datasets from object detection annotations.
 
 **GRAID** transforms raw object detection data into structured question-answer pairs that test various aspects of object localization, visual reasoning, spatial reasoning, and object relationship comprehension.
 
+## Abstract
+Vision Language Models (VLMs) achieve strong performance on many vision-language tasks but often struggle with spatial reasoning\textemdash{}a prerequisite for many applications. Empirically, we find that a dataset produced by a current training data generation pipeline has a 57.6\% human validation rate. These rates stem from current limitations: single-image 3D reconstruction introduces cascading modeling errors and requires wide answer tolerances, while caption-based methods require hyper-detailed annotations and suffer from generative hallucinations. We present GRAID, built on the key insight that qualitative spatial relationships can be reliably determined from 2D geometric primitives alone. By operating exclusively on 2D bounding boxes from standard object detectors, GRAID avoids both 3D reconstruction errors and generative hallucinations, resulting in datasets that are of higher quality than existing tools that produce similar datasets as validated by human evaluations. We apply our framework to the BDD100k, NuImages, and Waymo datasets, generating over 8.5 million high-quality VQA pairs creating questions spanning spatial relations, counting, ranking, and size comparisons. We evaluate one of the datasets and find it achieves 91.16\% human-validated accuracy\textemdash{}compared to 57.6\% on a dataset generated by recent work. % or recent work Critically, we demonstrate that when trained on GRAID data, models learn spatial reasoning concepts that generalize: models fine-tuned on 6 question types improve on over 10 held-out types, with accuracy gains of 47.5\% on BDD and 37.9\% on NuImages for Llama 3.2B 11B, and when trained on all questions types, achieve improvements on several existing benchmarks such as BLINK. The GRAID framework, datasets, and additional information can be found on our \href{ this https URL }{project page}.
+
 ## Dataset Details
 
 - **Total QA Pairs**: 13,855
@@ -142,14 +148,14 @@ train_data = dataset["train"]
 val_data = dataset["val"]
 
 # Example of accessing a sample
-sample = dataset["train"][0]  # or "val"
+sample = dataset["train"][0] # or "val"
 print(f"Question: {sample['question']}")
 print(f"Answer: {sample['answer']}")  
 print(f"Question Type: {sample['question_type']}")
 
 # The image is embedded as a PIL Image object
 image = sample["image"]
-image.show()  # Display the image
+image.show() # Display the image
 ```
 
 ## Dataset Schema
@@ -179,6 +185,13 @@ This dataset is derived from the Waymo Perception Dataset. Please refer to the [
 If you use this dataset in your research, please cite both the original dataset and the GRAID framework:
 
 ```bibtex
+@article{elmaaroufi2025graid,
+  title={GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation},
+  author={Elmaaroufi, Karim and Zheng, Jonathan and Xu, Haohan and Pan, Yang and Kim, Younghyun and Choi, Joshua and Amsalem, Yoav and Ma, Jianxin and Xu, Minjun and Liu, Fang and Liang, Ting and Singh, Kavit and Hwu, Wen-mei and Chen, Yida},
+  journal={arXiv preprint arXiv:2510.22118},
+  year={2025}
+}
+
 @dataset{graid_waymo,
     title={GRAID Waymo Perception Dataset Question-Answer Dataset},
     author={GRAID Framework},
@@ -197,4 +210,4 @@ If you use this dataset in your research, please cite both the original dataset
 
 ## Contact
 
-For questions about this dataset or the GRAID framework, please open an issue in the repository.
+For questions about this dataset or the GRAID framework, please open an issue in the repository.