# The iMet Collection 2019 Challenge Dataset

arXiv: https://arxiv.org/abs/1906.00901 · HTML: https://mlsanity.com/paper/1906.00901/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022)
- [The Met Dataset: Instance-level Recognition for Artworks](https://mlsanity.com/paper/2202.01747/) (2022)
- [Semi-supervised Human Pose Estimation in Art-historical Images](https://mlsanity.com/paper/2207.02976/) (2022)
- [Multimodal Metadata Assignment for Cultural Heritage Artifacts](https://mlsanity.com/paper/2406.00423/) (2024)
- [Understanding Museum Exhibits using Vision-Language Reasoning](https://mlsanity.com/paper/2412.01370/) (2024)
- [Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles](https://mlsanity.com/paper/2106.11359/) (2021)
- [Taming CLIP for Fine-grained and Structured Visual Understanding of Museum Exhibits](https://mlsanity.com/paper/2409.01690/) (2024)
- [ExpNet: A unified network for Expert-Level Classification](https://mlsanity.com/paper/2211.15672/) (2022)
- [Cleaning and Structuring the Label Space of the iMet Collection 2020](https://mlsanity.com/paper/2106.00815/) (2021)
- [Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition](https://mlsanity.com/paper/2403.05703/) (2024)

## Abstract (arXiv)

Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropolitan Museum of Art, and annotations are curated and verified by world-class museum experts. In addition, we also present the iMet Collection 2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim to spur the enthusiasm of the fine-grained visual recognition research community and advance the state-of-the-art in digital curation of museum collections.