--- annotations_creators: [] language: en size_categories: - 1K 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) ``` license: apache-2.0 --- # Dataset Card for neurips-2025-vision-papers ![image/png](visual_ai_neurips2025.gif) 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:** ``` @inproceedings{neurips2025, title={Neural Information Processing Systems}, year={2025}, organization={NeurIPS} } ``` ## Dataset Card Contact Harpreet Sahota