[Submitted on 4 Nov 2026]
Title:SD-LoRA: Training-Time Structural Distillation into LoRA for Few-Shot Vision-Language Adaptation
View a PDF of the paper titled SD-LoRA: Training-Time Structural Distillation into LoRA for Few-Shot Vision-Language Adaptation, by Mohammed Rahman Sherif Khan Mohammad and Ardhendu Behera and Sandip Pradhan and Swagat Kumar and Yonghuai Liu
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Abstract:Few-shot adaptation of vision-language models must balance two competing goals: learning fine-grained visual distinctions from very limited supervision and preserving the efficiency that makes frozen CLIP-like models attractive for deployment. Existing lightweight adaptation methods, including prompt tuning, LoRA, and cache-based adapters, typically supervise global image embeddings and therefore underuse token-level structure. Conversely, structure-aware methods exploit local evidence but often introduce additional inference-time computation. We propose SD-LoRA, a structural distillation framework that uses token-level graph reasoning only during training and transfers its effects into a lightweight LoRA-adapted CLIP model. During training, a heterogeneous graph teacher models patch-patch, global-local, and visual-text relations over high-resolution ViT tokens. Rather than retaining this graph at test time, SD-LoRA transfers its structural bias into the shared CLIP-LoRA backbone through implicit distillation and Prototype Predictive Alignment, which aligns graph-conditioned representations with support-derived class prototypes. At inference, the graph teacher is discarded; prediction requires only a single CLIP-LoRA forward pass and a cache-based classifier. Across 11 few-shot benchmarks, SD-LoRA improves over a strong CLIP-LoRA baseline by 2.4/1.8/3.0/2.4/2.0 percentage points in the 1/2/4/8/16-shot settings, respectively, without additional graph computation at test time. Results on base-to-novel generalization, cross-dataset transfer, out-of-distribution robustness, and calibration further show that training-time structural supervision improves both accuracy and transfer reliability. Code is available at this https URL.
Comments: Accepted in NeurIPS 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.07063 [cs.CV]
(or arXiv:2611.07063v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.07063
arXiv-issued DOI via DataCite
Submission history
From: Mohammed Rahman Sherif Khan Mohammad [view email] [v1] Wed, 4 Nov 2026 19:22:47 UTC (6,805 KB)
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