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Large Vision Foundation Models (VFMs) pretrained on massive datasets exhibitimpressive performance on various downstream tasks, especially with limitedlabeled target data. However, due to their high memory and computerequirements, these models cannot be deployed in resource constrained settings.This raises an important question: How can we utilize the knowledge from alarge VFM to train a small task-specific model for a new target task withlimited labeled training data? In this work, we answer this question byproposing a simple and highly effective task-oriented knowledge transferapproach to leverage pretrained VFMs for effective training of smalltask-specific models. Our experimental results on four target tasks underlimited labeled data settings show that the proposed knowledge transferapproach outperforms task-agnostic VFM distillation, web-scale CLIP pretrainingand supervised ImageNet pretraining by 1-10.5%, 2-22% and 2-14%, respectively.We also show that the dataset used for transferring knowledge has a significanteffect on the final target task performance, and propose an imageretrieval-based approach for curating effective transfer sets.

Label-efficient Training of Small Task-specific Models by Leveraging Vision Foundation Models