Li-Jun Zhao

Li-Jun Zhao, Zhen-Duo Chen, Zi-Chao Zhang et al.

Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes continually with limited novel class samples. A mainstream baseline for FSCIL is first to train the whole model in the base session, then freeze the feature extractor in the incremental sessions. Despite achieving high overall accuracy, most methods exhibit notably low accuracy for incremental classes. Some recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions, but they further cause the accuracy imbalance between past and current incremental classes. In this paper, we study the causes of such classification accuracy imbalance for FSCIL, and abstract them into a unified model bias problem. Based on the analyses, we propose a novel method to mitigate model bias of the FSCIL problem during training and inference processes, which includes mapping ability stimulation, separately dual-feature classification, and self-optimizing classifiers. Extensive experiments on three widely-used FSCIL benchmark datasets show that our method significantly mitigates the model bias problem and achieves state-of-the-art performance.

Li-Jun Zhao, Zhen-Duo Chen, Zhi-Yuan Xue et al.

Current fine-grained classification research primarily focuses on fine-grained feature learning. However, in real-world scenarios, fine-grained data annotation is challenging, and the features and semantics are highly diverse and frequently changing. These issues create inherent barriers between traditional experimental settings and real-world applications, limiting the effectiveness of conventional fine-grained classification methods. Although some recent studies have provided potential solutions to these issues, most of them still rely on limited supervised information and thus fail to offer effective solutions. In this paper, based on theoretical analysis, we propose a novel learning paradigm to break the barriers in fine-grained classification. This paradigm enables the model to progressively learn during inference, thereby leveraging cost-free data to more accurately represent fine-grained categories and adapt to dynamic semantic changes. On this basis, an efficient EXPloring and EXPloiting strategy and method (EXP2) is designed. Thereinto, useful inference data samples are explored according to class representations and exploited to optimize classifiers. Experimental results demonstrate the general effectiveness of our method, providing guidance for future in-depth understanding and exploration of real-world fine-grained classification.