Large vision-language models have achieved outstanding performance, but their
size and computational requirements make their deployment on
resource-constrained devices and time-sensitive tasks impractical. Model
distillation, the process of creating smaller, faster models that maintain the
performance of larger models, is a promising direction towards the solution.
This paper investigates the distillation of visual representations in large
teacher vision-language models into lightweight student models using a small-
or mid-scale dataset. Notably, this study focuses on open-vocabulary
out-of-distribution (OOD) generalization, a challenging problem that has been
overlooked in previous model distillation literature. We propose two principles
from vision and language modality perspectives to enhance student's OOD
generalization: (1) by better imitating teacher's visual representation space,
and carefully promoting better coherence in vision-language alignment with the
teacher; (2) by enriching the teacher's language representations with
informative and finegrained semantic attributes to effectively distinguish
between different labels. We propose several metrics and conduct extensive
experiments to investigate their techniques. The results demonstrate
significant improvements in zero-shot and few-shot student performance on
open-vocabulary out-of-distribution classification, highlighting the
effectiveness of our proposed approaches. Our code will be released at
https://github.com/xuanlinli17/large_vlm_distillation_oo