LEMoN: Label Error Detection using Multimodal Neighbors

Haoran Zhang, Aparna Balagopalan, Nassim Oufattole, Hyewon Jeong, Yan Wu, Jiacheng Zhu, Marzyeh Ghassemi

Keywords: label error detection, noisy labels, image captions

TL;DR: We propose a method to automatically identify label errors in image captions using the multimodal neighborhood of image-caption pairs.

Abstract: Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the web, and contain many mislabeled instances. In order to improve the reliability of downstream models, it is important to identify and filter images with incorrect captions. However, beyond filtering based on image-caption embedding similarity, no prior works have proposed other methods to filter noisy multimodal data, or concretely assessed the impact of noisy captioning data on downstream training. In this work, we propose, theoretically justify, and empirically validate LEMoN, a method to automatically identify label errors in image-caption datasets. Our method leverages the multimodal neighborhood of image-caption pairs in the latent space of contrastively pretrained multimodal models to automatically identify label errors. Through empirical evaluations across eight datasets and ten baselines, we find that LEMoN outperforms the baselines by over 3% in label error detection, and that training on datasets filtered using our method improves downstream captioning performance by 2 BLEU points.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 3263