#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# Dataset Card for neurips-2025-vision-papers
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1134 samples.
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/visual_ai_at_neurips2025")
# Launch the App
session = fo.launch_app(dataset)
```
## Dataset Details
### Dataset Description
This dataset contains NeurIPS 2025 accepted papers focused on computer vision and related fields, enriched with arXiv metadata and first-page images. It includes papers from multiple vision-related categories including Computer Vision (cs.CV), Multimedia (cs.MM), Image and Video Processing (eess.IV), Graphics (cs.GR), and Robotics (cs.RO). Each entry includes paper metadata, abstracts, author information, and a high-resolution (500 DPI) PNG image of the paper’s first page.
- **Curated by:** Harpreet Sahota
- **Language(s) (NLP):** en
- **License:** Apache 2.0
### Dataset Sources
- **Original Data Source:** NeurIPS 2025 Conference (https://neurips.cc/virtual/2025/calendar)
- **arXiv API:** https://arxiv.org/
## Uses
### Direct Use
This dataset is suitable for:
- Analyzing trends in computer vision research at NeurIPS 2025
- Vision-Language Model (VLM) analysis of paper content
- OCR and text extraction from academic papers
- Building search and recommendation systems for academic papers
- Studying paper formatting, structure, and visual presentation
- Training models to understand academic paper layouts
### Out-of-Scope Use
This dataset should not be used for:
- Representing the complete NeurIPS 2025 corpus (only vision-related papers with arXiv IDs)
- Papers without arXiv IDs are not included
- Full paper content analysis (only first pages are included)
- Citation analysis (references are not included)
## Dataset Structure
The dataset contains the following fields:
- **filepath**: Path to the first-page PNG image (500 DPI)
- **type**: Paper presentation type (e.g., “Poster”, “Oral”)
- **name**: Paper title
- **virtualsite_url**: URL to the paper on NeurIPS virtual site
- **abstract**: Paper abstract
- **arxiv_id**: arXiv identifier (e.g., “2301.12345v2”)
- **arxiv_authors**: List of paper authors from arXiv
- **arxiv_category**: Classification field with paper category (cs.CV, cs.MM, eess.IV, cs.GR, or cs.RO)
## Dataset Creation
### Curation Rationale
This dataset was created to provide a focused collection of vision-related papers from NeurIPS 2025 with high-quality first-page images for multimodal analysis. The motivation was to enable researchers and practitioners to:
1. Analyze paper content using Vision-Language Models
2. Study trends in computer vision research
3. Build tools for academic paper understanding
### Source Data
#### Data Collection and Processing
1. **Initial Collection**: Paper metadata scraped from NeurIPS 2025 virtual conference site
2. **arXiv Matching**: Papers matched with arXiv using title and author matching algorithms
3. **Category Filtering**: Filtered to include only vision-related categories (cs.CV, cs.MM, eess.IV, cs.GR, cs.RO) with valid arXiv IDs
4. **PDF Download**: First pages downloaded from arXiv (https://arxiv.org/pdf/{arxiv_id}.pdf)
5. **Image Conversion**: PDFs converted to PNG images at 500 DPI using pdf2image
6. **Quality**: 500 DPI ensures readability of 10pt font common in academic papers
#### Who are the source data producers?
- **NeurIPS 2025 Conference**: Original paper metadata and acceptance decisions
- **arXiv**: Paper PDFs and metadata
- **Paper Authors**: Original paper content
### Annotations
#### Annotation process
The `arxiv_category` field represents the primary arXiv category assigned by paper authors during submission. No additional manual annotations were added.
## Bias, Risks, and Limitations
**Limitations:**
- Only includes papers with arXiv IDs (some NeurIPS papers may not be on arXiv)
- Only includes first page (no full paper content)
- Limited to specific vision-related categories
- arXiv matching may have errors or mismatches
- Images are high resolution (500 DPI) resulting in larger file sizes
**Biases:**
- Excludes papers without arXiv presence
- May underrepresent certain research areas or institutions with different publication practices
- Category classification reflects author self-assignment on arXiv
### Recommendations
Users should be made aware that:
- This is not a complete representation of NeurIPS 2025 papers
- arXiv matching was automated and may contain errors
- Only first pages are available (for full papers, refer to arXiv or NeurIPS proceedings)
- High DPI images require significant storage space
## Citation
**NeurIPS 2025:**
```default
@inproceedings{neurips2025,
title={Neural Information Processing Systems},
year={2025},
organization={NeurIPS}
}
```
## Dataset Card Contact
Harpreet Sahota