Hyperbolic Displacement-Constrained Adaptation for CLIP-Based Class-Incremental Learning

Class-incremental learning aims to learn a stream of new classes while preserving knowledge from prior tasks, but suffers from catastrophic forgetting when the model is adapted across sessions. Recently, approaches leveraging vision-language pretrained models such as CLIP have gained increasing popularity in this setting, due to the strong transferable representations of foundation models. Most CLIP-based approaches resist forgetting by retaining data-derived class memory such as replay samples, visual prototypes, or class distribution information. In contrast, we ask whether stable adaptation can be achieved by constraining adapter-induced feature displacement without retaining any data-derived class memory. We propose \textbf{H}yperbolic \textbf{D}isplacement-\textbf{C}onstrained \textbf{A}daptation (HDCA), which treats forgetting as a consequence of uncontrolled residual displacement induced by sequential adapter updates. HDCA keeps the direction of each adapter update but compresses its radial magnitude, so small updates remain almost unchanged while large or accumulated updates are suppressed. At the per-task level, a hyperbolic low-rank adapter makes each task residual grow sublinearly. At the multi-task level, a cumulative compression stage further suppresses the summed residual across sessions. On the classifier side, HDCA introduces a parameter-free Poincar\'e distance classifier that preserves cosine-based ranking for fixed adapted features but reshapes the training gradient in high-similarity regions. We conduct experiments on five standard class-incremental datasets, and the results comprehensively validate the effectiveness of HDCA without retaining any data-derived class memory during training and inference.