GIP-GEO: Geometric Self-evaluation for Few-shot Class-incremental Learning

Few-shot class-incremental learning requires learning novel classes from few labeled examples while retaining previously learned classes. Class-nameconditioned diffusion supports training-free adaptation, but its descriptions may not reflect the observed support images, and its generations are typically used without an explicit alignment check. GIP-Geo addresses both limitations. Grounded Iterative Prompting conditions the language model on support images and nearby base-class names, producing candidate descriptions of observed, discriminative attributes. Geometric self-evaluation then scores agreement among each candidate’s text, real-support prototype, and generated prototype via alignment-regularized volume (ARV), which combines a kernel Gram determinant with pairwise dispersion to avoid a degenerate zero score. The same score selects one candidate and its fusion weight with real supports. On CIFAR-100, miniImageNet, and CUB-200, exhaustive GIP-Geo improves average accuracy over the controlled CD-FSCIL baseline without incremental network training; an optional score proxy reduces generation cost while CLIP and diffusion remain frozen.