Summary of Lemon: Label Error Detection Using Multimodal Neighbors, by Haoran Zhang et al.
LEMoN: Label Error Detection using Multimodal Neighbors
by Haoran Zhang, Aparna Balagopalan, Nassim Oufattole, Hyewon Jeong, Yan Wu, Jiacheng Zhu, Marzyeh Ghassemi
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to identify and filter noisy image-caption pairs in large multimodal datasets. The proposed method, LEMoN, leverages the latent space of contrastively pretrained multimodal models to automatically detect label errors. Unlike previous methods that rely solely on image-caption embedding similarity, LEMoN provides a more comprehensive framework for identifying mislabeled examples. Experimental results show that LEMoN outperforms baselines in label error identification and improves downstream classification and captioning performance when training on filtered datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sure that image and text pairs are correctly matched together. This is important because incorrect matches can affect how well computer models work. The researchers came up with a new way to find mistakes in these pairings, called LEMoN. They tested their method and found it works better than other approaches. When they used this corrected data to train models, they got better results. |
Keywords
* Artificial intelligence * Classification * Embedding * Latent space
