Dynamic Semantic Evolution for Semi-Supervised Few-Shot Incremental Learning

Semi-Supervised Few-Shot Class Incremental Learning (SSFSCIL) aims to continuously acquire novel concepts from scarce annotations and abundant unlabeled data while preserving previously learned knowledge. However, existing approaches mainly rely on static pseudo-labeling and incremental adaptation, which struggle with three fundamental challenges: unreliable semantic discovery under limited supervision, unstable representation evolution across sessions, and insufficient decision boundary characterization with few-shot samples. In this paper, we introduce DSE-SSF, a novel framework that reformulates continuous SSFSCIL as a process of dynamic semantic evolution rather than sequential parameter optimization. Based on this insight, DSE-SSF establishes a closed-loop learning paradigm consisting of semantic discovery, knowledge stabilization, and boundary refinement, implemented by three novel mechanisms: (1) Dynamic Semantic Graph Evolution progressively uncovers reliable semantic structures from unlabeled data through evolving manifold propagation; (2) Adaptive Dual-Expert Stabilization leverages temporal consistency and adaptive uncertainty calibration to prevent incremental knowledge drift; and (3) Boundary-aware Feature Manifold Exploration explicitly investigates uncertain feature regions to construct robust decision boundaries under sparse supervision. By jointly modeling the evolution of semantic topology, representation stability, and boundary geometry, DSE-SSF enables reliable knowledge acquisition throughout continuous incremental learning. Extensive experiments on SSFSCIL benchmarks demonstrate that DSE-SSF consistently outperforms existing methods, validating the effectiveness of dynamic semantic evolution for long-term few-shot visual learning.