WP-FSCIL: A Well-Prepared Few-shot Class-incremental Learning Framework for Pill Recognition.

Abstract

Few-shot Class-incremental Pill Recognition (FSCIPR) aims to develop an automatic pill recognition system that requires only a few training data and can continuously adapt to new classes, providing technical support for applications in hospitals, portable apps, and assistance for visually impaired individuals. This task faces three core challenges: overfitting, fine-grained classification problems, and catastrophic forgetting. We propose the Well-Prepared Few-shot Class-incremental Learning (WP-FSCIL) framework, which addresses overfitting through a parameter-freezing strategy, enhances the robustness and discriminative power of backbone features with Center-Triplet (CT) loss and supervised contrastive loss for fine-grained classification, and alleviates catastrophic forgetting using a multi-dimensional Knowledge Distillation (KD) strategy based on flexible Pseudo-feature Synthesis (PFS). By flexibly synthesizing any number of old-class features, the PFS strategy resolves the issue of insufficient samples in the KD process, enabling Response-based KD (KD1) and Relation-based KD (KD2) to comprehensively preserve old knowledge. The effectiveness of WP-FSCIL has been validated through experiments conducted on two publicly available pill datasets. These experiments show that WP-FSCIL outperforms existing state-of-the-art methods, demonstrating its superior performance.