CenWrite: Support-Centered LoRA with Boundary Updates for Cross-Domain Few-Shot Learning

Cross-domain few-shot learning adapts a pretrained vision model to a new domain with scarce labels. Such adaptation must use the same small support set to accommodate domain differences and distinguish target classes. It must also produce reliable predictive probabilities despite having only very limited labeled evidence about the target domain. Low-rank adaptation (LoRA) offers a parameter-efficient approach, but uncentered support activations make the support-set mean response and sample-specific deviations jointly shape its low-rank factors. Direct centering changes this learning path but also removes the shared response from the forward pass. We propose CenWrite to center factor learning while preserving the shared response and enabling independent boundary adjustment. CenWrite learns low-rank factors from centered support activations, restores the shared response through stop-gradient compensation, and learns a separate boundary residual. Experiments across multiple domains demonstrate that CenWrite achieves higher average classification performance and lower probability errors than the compared parameter-efficient baselines. Ablation studies further corroborate that centering and forward compensation improve average probability quality, while the boundary residual improves average classification performance.