AlphaWiSE: Adaptive Weight Interpolation for Continual Multimodal Representation Learning
Multimodal models such as CLIP learn a shared embedding space for cross-modal retrieval, but continual adaptation to sequentially arriving data can disrupt the cross-modal alignment acquired from earlier phases. Conventional continual-learning methods return a single checkpoint, which commits every retrieval direction to the same stability-plasticity trade-off. We propose AlphaWiSE, a post-hoc weight-space interpolation method that composes two frozen source checkpoints. For each aligned parameter tensor identified by its checkpoint key, AlphaWiSE fits one scalar interpolation coefficient shared by all tensor entries. The coefficients are fitted on a smaller exemplar memory and used to ma...
| Authors | Sarthak Jain, Qiran Hu, Zhen Zhu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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