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Datasets:
choucsan
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CVPR_Papers

Tasks:
Text Classification
Question Answering
Summarization
Modalities:
Document
Languages:
English
Size:
10K<n<100K
Tags:
computer-vision
paper
cvpr
ocr
pdf
research
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metadata 
language:
  - en
license: mit
task_categories:
  - text-classification
  - question-answering
  - summarization
  - text-retrieval
tags:
  - computer-vision
  - paper
  - cvpr
  - ocr
  - pdf
  - research
size_categories:
  - 10K<n<100K

CVPR Papers

CVPR Papers

GitHub Hugging Face Blog License

Since 2013, deep learning has revolutionized computer vision, with CVPR (IEEE Conference on Computer Vision and Pattern Recognition) serving as the premier venue documenting this transformative journey. From AlexNet's breakthrough to the rise of Transformers, CVPR papers chronicle the complete trajectory of computer vision advancement.

CVPR Papers is a comprehensive dataset containing all papers from CVPR 2013 to present, including metadata and PDF files. It is designed for literature review, trend analysis, citation network construction, and various research tasks in computer vision.

Pipeline

The dataset is constructed through a systematic multi-stage pipeline:

  1. Web Scraping: Extract paper listings from CVF Open Access repository
  2. Metadata Extraction: Parse HTML to extract titles, authors, PDF links, and BibTeX citations
  3. Abstract Retrieval: Fetch abstracts from individual paper detail pages
  4. PDF Download: Concurrently download all paper PDF files
  5. Data Validation: Verify data integrity and format consistency

Dataset Structure 

CVPR_Papers/
├── 2013/
│   ├── pdf/                    # PDF files for all papers
│   │   ├── paper1.pdf
│   │   ├── paper2.pdf
│   │   └── ...
│   └── meta.jsonl         # Metadata 
│ 
├── 2014/
│   ├── pdf/
│   └── meta.jsonl
└── ...

Dataset Overview

  • Total Papers: 18,452 (CVPR 2013-2025, continuously expanding)
  • Data Format: JSONL for metadata, PDF for full papers, organized by year
  • Source: CVF Open Access
Field Type Description
title string Paper title
authors string Comma-separated list of authors
abstract string Paper abstract
pdf_path string Relative path to PDF file
download_url string Direct download URL for PDF
bibtex string BibTeX citation string

Example Entry:

{
  "title": "Deformable Spatial Pyramid Matching for Fast Dense Correspondences",
  "authors": "Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman",
  "abstract": "We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences...",
  "pdf_path": "2013/pdf/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf",
  "download_url": "https://openaccess.thecvf.com/content_cvpr_2013/papers/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf",
  "bibtex": "@InProceedings{Kim_2013_CVPR,author = {Kim, Jaechul and Liu, Ce and Sha, Fei and Grauman, Kristen},title = {Deformable Spatial Pyramid Matching for Fast Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}"
}

Quick Start

Installation 

pip install huggingface_hub requests

Load Metadata 

from huggingface_hub import hf_hub_download
import json

# Download metadata for a specific year
year = "2013"
meta_path = hf_hub_download(
    repo_id="choucsan/CVPR_Papers",
    filename=f"{year}/meta.jsonl",
    repo_type="dataset",
)

# Load metadata
papers = []
with open(meta_path, 'r', encoding='utf-8') as f:
    for line in f:
        papers.append(json.loads(line))

print(f"Loaded {len(papers)} CVPR {year} papers")

Download PDF Files

Each paper has a download_url field pointing to the original PDF on CVF Open Access:

import requests
import os

# Create output directory
os.makedirs(f"cvpr_{year}_pdfs", exist_ok=True)

# Download a specific paper
paper = papers[0]
response = requests.get(paper['download_url'])

filename = os.path.basename(paper['pdf_path'])
with open(f"cvpr_{year}_pdfs/{filename}", 'wb') as f:
    f.write(response.content)

print(f"Downloaded: {filename}")

# Download all papers for a year (optional)
for paper in papers:
    if paper.get('download_url'):
        response = requests.get(paper['download_url'])
        filename = os.path.basename(paper['pdf_path'])
        with open(f"cvpr_{year}_pdfs/{filename}", 'wb') as f:
            f.write(response.content)

Applications

PDF Access

  • Direct Download: Use download_url to download PDFs directly from CVF Open Access
  • Full Text Analysis: Extract text from PDFs for detailed content analysis
  • Figure Extraction: Extract figures, tables, and equations from papers
  • Layout Analysis: Analyze paper structure and formatting patterns
  • OCR Processing: Optical character recognition for scanned documents

Research Applications

  • Literature Review: Rapid retrieval of papers in specific domains for comprehensive literature review
  • Trend Analysis: Analyze research hotspots and development trends in computer vision over the past decade
  • Citation Networks: Build and analyze paper citation networks based on BibTeX data
  • Knowledge Graphs: Construct knowledge graphs connecting papers, authors, institutions, and concepts
  • Recommendation Systems: Build paper recommendation systems based on content similarity

Download

Contact

choucisan@gmail.com