THMM-CLIP: Task-Guided Hierarchical Multi-Modal Alignment for Rehearsal-Free Class Incremental Learning

Class incremental learning (CIL) requires models to acquire knowledge from sequential tasks containing non-overlapping classes while avoiding catastrophic forgetting. While vision-language foundation models like CLIP demonstrate remarkable potential for CIL through their pre-trained cross-modal alignment capabilities, existing CLIP-based approaches critically overlook the progressive degradation of visual representations in incremental scenarios . Through feature space analysis, we identify a crucial dichotomy : textual embeddings maintain stable discriminative power across sequential tasks, whereas visual features exhibit progressive deterioration manifested by intra-task confusion (ambiguous decision boundaries between co-occurring classes) and inter-task interference (semantic collision between historical and novel categories). To address these dual challenges, we propose task-guided hierarchical multi-modal alignment (THMM-CLIP), a framework that establishes persistent visual-textual coherence through hierarchical multi-modal alignment (HMA) and robust prompt selection (RPS). HMA adapts lightweight task-specific prompt vectors to dynamically recalibrate the CLIP image encoder, thereby achieving: (i) intra-task alignment, (ii) inter-task discriminability alignment, and (iii) global structural alignment with textual features. RPS incorporates a dual-level task identifier that integrates class-level and task-level representative features to ensure precise prompt retrieval during inference. Ablation studies validate all components’ contributions, while t-SNE visualizations, confusion matrices, and Grad-CAM analyses confirm strengthened cross-modal alignment.