Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based continual learning methods effectively address the issue of catastrophic forgetting through prompt expansion and selection. However, existing approaches often suffer from low accuracy in prompt selection, which can result in the model receiving biased knowledge and making biased predictions. To address this issue, we propose the Multiple Queries with Multiple Keys (MQMK) prompt matching paradigm for precise prompt selection. The goal of MQMK is to select the prompts whose training data distribution most closely matches that of the test sample. Specifically, Multiple Queries enable precise breadth search by introducing task-specific knowledge, while Multiple Keys perform deep search by representing the feature distribution of training samples at a fine-grained level. Each query is designed to perform local matching with a designated task to reduce interference across queries. Experiments show that MQMK enhances the prompt matching rate by over 30\% in challenging scenarios and achieves state-of-the-art performance on three widely adopted continual learning benchmarks. The code is available at https://github.com/DunweiTu/MQMK.

The aim of few-shot class-incremental learning (FSCIL) is to continually classify new classes from limited samples without forgetting previously learned classes. Existing deep learning-based methods for fine-grained vehicle recognition are mostly deployed after a single model training session on a complete, large dataset. However, the real-world scenario involves constant emergence of new vehicle categories with limited labeled data. Retraining models at scale for every new vehicle release becomes impractical and this poses a FSCIL challenge. To address this issue, this paper proposes the Contrastive Center Optimization (CCO) method. Based on an Incremental-frozen framework, CCO prevents the forgetting of previous learning during the incremental sessions. In the base training session, CCO uses contrastive learning after data augmentation to indirectly decrease intra-class distances and increase inter-class distances. Additionally, we apply center loss to further directly optimize intra-class and inter-class distances. This approach not only enhances classification accuracy, but also creates sufficient space for the insertion of new classes. Extensive experiments on two fine-grained vehicle datasets demonstrate that our CCO achieves new state-of-the-art performance, confirming the importance of decreasing intra-class distances and increasing inter-class distances.