Few-Shot Class-Incremental Learning via Compact and Separable Features for Fine-Grained Vehicle Recognition
De-Wang Li, Hua Huang
Abstract
Most of the existing deep learning-based fine-grained vehicle recognition methods collect a large-scale training set in advance and train a model based on the closed-world assumption. However, in the real world, new classes of vehicles are released over time but it is difficult to collect sufficient labeled data for new classes, which results in a typical few-shot class-incremental learning problem (FSCIL). To solve this problem, this work proposes a compact and separable feature learning method (CSFL) which exploits a decoupled learning scheme to prevent the feature extractor from updating during class-incremental learning. CSFL trains an initial model to learn discriminative features of fine-grained vehicles using deep metric learning. Then an incremental linear discriminant analysis algorithm is applied to the learned features to further discriminate potential confused classes. Specifically, the decoupled components share the same objective of enhancing intra-class compactness and inter-class separability, which is beneficial for classification. Extensive experiments on three fine-grained vehicle datasets demonstrate that the proposed CSFL achieves better results than state-of-the-art incremental learning methods, validating the importance of compact and separable features in the problem of FSCIL.