Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss

• URL: http://arxiv.org/abs/2310.08206v2
• Date: Tue, 19 Dec 2023 07:21:03 GMT
• Title: Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss
• Authors: Jinye Yang, Ji Xu, Di Wu, Jianhang Tang, Shaobo Li, Guoyin Wang
• Abstract summary: Long-tailed (LT) classification is an unavoidable and challenging problem in the real world. We propose a novel long-tailed classification framework, aiming to build a multi-granularity classification model by means of invariant feature learning. Our approach achieves state-of-the-art performance on both existing benchmarks ImageNet-GLT and MSCOCO-GLT.
• Score: 21.302508774614154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-tailed (LT) classification is an unavoidable and challenging problem in the real world. Most existing long-tailed classification methods focus only on solving the class-wise imbalance while ignoring the attribute-wise imbalance. The deviation of a classification model is caused by both class-wise and attribute-wise imbalance. Due to the fact that attributes are implicit in most datasets and the combination of attributes is complex, attribute-wise imbalance is more difficult to handle. For this purpose, we proposed a novel long-tailed classification framework, aiming to build a multi-granularity classification model by means of invariant feature learning. This method first unsupervisedly constructs Coarse-Grained forest (CLF) to better characterize the distribution of attributes within a class. Depending on the distribution of attributes, one can customize suitable sampling strategies to construct different imbalanced datasets. We then introduce multi-center loss (MCL) that aims to gradually eliminate confusing attributes during feature learning process. The proposed framework does not necessarily couple to a specific LT classification model structure and can be integrated with any existing LT method as an independent component. Extensive experiments show that our approach achieves state-of-the-art performance on both existing benchmarks ImageNet-GLT and MSCOCO-GLT and can improve the performance of existing LT methods. Our codes are available on GitHub: \url{https://github.com/jinyery/cognisance}

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