Voxel51/visual_ai_at_neurips2025

Dataset Card for neurips-2025-vision-papers

This is a FiftyOne dataset with 1134 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

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:

@inproceedings{neurips2025,
  title={Neural Information Processing Systems},
  year={2025},
  organization={NeurIPS}
}

Dataset Card Contact

Harpreet Sahota