Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition

• URL: http://arxiv.org/abs/2307.00880v1
• Date: Mon, 3 Jul 2023 09:20:28 GMT
• Title: Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition
• Authors: Chao Liang, Zongxin Yang, Linchao Zhu, Yi Yang
• Abstract summary: This paper focuses on reducing noise based on some inherent properties of multi-label classification and long-tailed learning under noisy cases. We propose a Stitch-Up augmentation to synthesize a cleaner sample, which directly reduces multi-label noise. A Heterogeneous Co-Learning framework is further designed to leverage the inconsistency between long-tailed and balanced distributions.
• Score: 70.00984078351927
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the applications of learning-based models. Although many deep learning based methods have been proposed for handling long-tailed multi-label recognition or label noise respectively, learning with noisy labels in long-tailed multi-label visual data has not been well-studied because of the complexity of long-tailed distribution entangled with multi-label correlation. To tackle such a critical yet thorny problem, this paper focuses on reducing noise based on some inherent properties of multi-label classification and long-tailed learning under noisy cases. In detail, we propose a Stitch-Up augmentation to synthesize a cleaner sample, which directly reduces multi-label noise by stitching up multiple noisy training samples. Equipped with Stitch-Up, a Heterogeneous Co-Learning framework is further designed to leverage the inconsistency between long-tailed and balanced distributions, yielding cleaner labels for more robust representation learning with noisy long-tailed data. To validate our method, we build two challenging benchmarks, named VOC-MLT-Noise and COCO-MLT-Noise, respectively. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method. Compared to a variety of baselines, our method achieves superior results.

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