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Prototype-based classifier learning for long-tailed visual recognition

Science China(Information Sciences) 2022年第6期65卷 62-76页

作者： Xiu-Shen WEI Shu-Lin XU Hao CHEN Liang XIAO Yuxin PENG School of Computer Science and Engineering Nanjing University of Science and Technology Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Wangxuan Institute of Computer Technology Peking University State Key Laboratory of Integrated Services Networks Xidian University

In this paper, we tackle the long-tailed visual recognition problem from the categorical prototype perspective by proposing a prototype-based classifier learning(PCL) method. Specifically, thanks to the generalization ability and robustness, categorical prototypes reveal their advantages of representing the category semantics. Coupled with their class-balance characteristic, categorical prototypes also show potential for handling data imbalance. In our PCL, we propose to generate the categorical classifiers based on the prototypes by performing a learnable mapping function. To further alleviate the impact of imbalance on classifier generation, two kinds of classifier calibration approaches are designed from both prototype-level and example-level aspects. Extensive experiments on five benchmark datasets, including the large-scale iNaturalist, Places-LT,and ImageNet-LT, justify that the proposed PCL can outperform state-of-the-arts. Furthermore, validation experiments can demonstrate the effectiveness of tailored designs in PCL for long-tailed problems.

关键词： long-tailed distribution categorical prototype classifier generation classifier calibration class imbalance

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Residual diverse ensemble for long-tailed multi-label text classification

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Science China(Information Sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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long-tailed object detection of kitchen waste with class-instance balanced detector

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Science China(Technological Sciences) 2023年第8期66卷 2361-2372页

作者： FANG LeYuan TANG Qi OUYANG LiHan YU JunWu LIN JiaXing DING ShuaiYu TANG Lin College of Electrical and Information Engineering Hunan UniversityChangsha 410082China College of Environmental Science and Engineering Hunan UniversityChangsha 410082China

Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual ***,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass *** address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen ***-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during ***,CIB-Det comprehensively and adaptively addresses the long-tailed *** experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.

关键词： kitchen waste detection and classification object detection long-tailed distribution convolutional neural networks

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