تقرير
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
| العنوان: | Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning |
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| المؤلفون: | Zhao, Zihua, Chen, Mengxi, Dai, Tianjie, Yao, Jiangchao, han, Bo, Zhang, Ya, Wang, Yanfeng |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Noisy correspondence that refers to mismatches in cross-modal data pairs, is prevalent on human-annotated or web-crawled datasets. Prior approaches to leverage such data mainly consider the application of uni-modal noisy label learning without amending the impact on both cross-modal and intra-modal geometrical structures in multimodal learning. Actually, we find that both structures are effective to discriminate noisy correspondence through structural differences when being well-established. Inspired by this observation, we introduce a Geometrical Structure Consistency (GSC) method to infer the true correspondence. Specifically, GSC ensures the preservation of geometrical structures within and between modalities, allowing for the accurate discrimination of noisy samples based on structural differences. Utilizing these inferred true correspondence labels, GSC refines the learning of geometrical structures by filtering out the noisy samples. Experiments across four cross-modal datasets confirm that GSC effectively identifies noisy samples and significantly outperforms the current leading methods. Comment: 10 pages, 5 figures, received by IEEE/CVF Computer Science and Pattern Recognition |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.16996 |
| رقم الأكسشن: | edsarx.2405.16996 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |