PAIRS DATA AND CODE

PAIRS: Panofskian Art Interpretation and Retrieval System Repository Contents This repository contains the complete implementation, datasets, and experimental data for the paper "PAIRS: A Panofskian Art Interpretation and Retrieval System Based on Multi-Agent Collaboration". File Structure File Size Description code.zip - Complete source code for PAIRS annotation-data.zip - Pre-computed annotations for all artworks semart-images-1.zip - SemArt dataset images (Part 1) semart-images-2.zip - SemArt dataset images (Part 2) semart-files.zip - SemArt dataset metadata and query files 1. Code (code.zip) Complete implementation of the PAIRS framework. Structure code/ ├── art-retrieval-research/ │ └── data_annotation_system/ # MC-ArtAnnotate algorithm implementation ├── image_retrieval_platform/ # FastAPI-based retrieval platform └── experiment/ # Experiment scripts and baselines Components Data Annotation System: Multi-agent collaborative annotation framework with six specialized agents (Form Analysis, Subject Identification, Symbolism, Cultural Context, Technical Material, Coordinator). Image Retrieval Platform: REST API for art image retrieval with LLM-based verification. Experiment Code: Scripts for reproducing all experimental results, including baseline implementations (GPT-4o, Claude Sonnet 4.5, InternVL-2.5, BLIP-2, CLIP-Large). See code/README.md for detailed usage instructions. 2. Annotation Data (annotation-data.zip) Pre-computed multi-agent annotations for all artworks in the evaluation datasets. Contents Structured annotations following Panofsky's three-level iconographic framework Global annotations (form, subject, symbolism, cultural context, technical analysis) Regional annotations with spatial coordinates Vector embeddings (BGE-large-en-v1.5, 1024 dimensions) 3. SemArt Dataset The SemArt benchmark is split into three files due to size constraints. Images semart-images-1.zip: Artwork images (Part 1) semart-images-2.zip: Artwork images (Part 2) Metadata (semart-files.zip) Image metadata (title, artist, date, type) Official train/test splits 100 evaluation queries with ground-truth annotations Dataset Scales We construct three evaluation scales from SemArt: Dataset Images Purpose data-100 100 Rapid validation data-1069 1,069 Primary benchmark (complete test set) data-5000 5,000 Scalability testing License SemArt is released under CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial). See the original dataset page for details: SemArt Benchmark System Requirements Python 3.10+ CUDA-compatible GPU (recommended) 32GB+ RAM for large-scale experiments API keys: Anthropic, OpenAI, or compatible LLM services