# CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

Source: [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](<https://www.sota2.com/research/paper/68fa6c631e209131cea33c50>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:09:48.320Z

Authors: Marcos V. Conde, Kerem Turgutlu

Published: 2022-04-29T17:17:24.000Z

## Abstract

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. To the best of our knowledge, we are one of the first methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural network on a variety of artwork images and text descriptions pairs. CLIP is able to learn directly from free-form art descriptions, or, if available, curated fine-grained labels. Model's zero-shot capability allows predicting accurate natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset, which we consider the largest annotated artwork dataset. In this benchmark we achieved competitive results using only self-supervision.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Artist Influence Prediction on WIB-100 balanced](<https://www.sota2.com/research/sota/artist-influence-prediction-on-wib-100-balanced.md>): Artist Influence Prediction; WIB-100 balanced; Precision: 55.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.14244>)

[PDF](<https://arxiv.org/pdf/2204.14244.pdf>)