Cleaning and Structuring the Label Space of the iMet Collection 2020

TL;DR

This paper improves the iMet 2020 art attribution dataset by cleaning noisy labels and structuring their semantic relationships, enhancing its utility for fine-grained recognition tasks.

Contribution

It introduces a novel approach to cleaning and organizing the label space of the iMet 2020 dataset, leveraging semantic relationships between labels.

Findings

Cleaner labels improve model performance.

Structured label relationships enhance recognition accuracy.

Code and dataset are publicly available.

Abstract

The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution recognition, but we believe it has yet to reach its true potential. We document the unique properties of the dataset and observe that many of the attribute labels are noisy, more than is implied by the dataset description. Oftentimes, there are also semantic relationships between the labels (e.g., identical, mutual exclusion, subsumption, overlap with uncertainty) which we believe are underutilized. We propose an approach to cleaning and structuring the iMet 2020 labels, and discuss the implications and value of doing so. Further, we demonstrate the benefits of our proposed approach through several experiments. Our code and cleaned labels are available at https://github.com/sunniesuhyoung/iMet2020cleaned.