Datasets:

choucsan
/

CVPR_Papers

language:
  - en
license: mit
task_categories:
  - text-classification
  - question-answering
  - summarization
  - text-retrieval
tags:
  - computer-vision
  - paper
  - cvpr
  - ocr
  - pdf
  - research
size_categories:
  - 100K<n<1M

CVPR Papers

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:

Web Scraping: Extract paper listings from CVF Open Access repository
Metadata Extraction: Parse HTML to extract titles, authors, PDF links, and BibTeX citations
Abstract Retrieval: Fetch abstracts from individual paper detail pages
PDF Download: Concurrently download all paper PDF files
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: 1,011 (CVPR 2013: 471, CVPR 2014: 540, 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
`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",
  "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

Download Dataset

Download a specific year from Hugging Face:

from huggingface_hub import snapshot_download

# Download a specific year (e.g., 2013)
year = "2013"
snapshot_download(
    repo_id="choucsan/CVPR_Papers",
    repo_type="dataset",
    local_dir="CVPR_Papers",
    allow_patterns=[f"{year}/*"],
)

Load Data

import json

# Load metadata for a specific year
year = "2013"
papers = []
with open(f'CVPR_Papers/{year}/meta.jsonl', 'r', encoding='utf-8') as f:
    for line in f:
        papers.append(json.loads(line))

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

# View first paper
first_paper = papers[0]
print(f"Title: {first_paper['title']}")
print(f"Authors: {first_paper['authors']}")
print(f"Abstract: {first_paper['abstract'][:200]}...")

Access PDF Files

import os

# List all PDF files for a specific year
year = "2013"
pdf_dir = f"CVPR_Papers/{year}/pdf"
pdf_files = [f for f in os.listdir(pdf_dir) if f.endswith('.pdf')]
print(f"Found {len(pdf_files)} PDF files")

# Read a specific paper
paper = papers[0]
pdf_path = os.path.join("CVPR_Papers", paper['pdf_path'])
print(f"PDF path: {pdf_path}")

Applications

PDF Files

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

Hugging Face: datasets/choucsan/CVPR_Papers

Contact

choucisan@gmail.com