Breaking Fine-Grained Classification Barriers with Cost-Free Data in Few-Shot Class-Incremental Learning

Current fine-grained classification research mainly concentrates on fine-grained feature learning, but in real-world applications, the bigger issue often lies in the data. Fine-grained data annotation is challenging, and the features and semantics are highly diverse and frequently changing, making traditional methods less effective in real-world scenarios. Although some studies have provided potential solutions to this issue, most are limited to making use of limited supervised information. In this paper, we propose a novel learning paradigm to break barriers in fine-grained classification. It enables the model to learn beyond the standard training phase and benefit from cost-free data encountered during system operation. On this basis, an efficient EXPloring and EXPloiting strategy and method (EXP2) is designed. Thereinto, before the final classification results are obtained, representative inference data samples are explored according to class templates and exploited to optimize classifiers. Experimental results demonstrate the general effectiveness of EXP2.

Bias Mitigating Few-Shot Class-Incremental Learning

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.