  Vision-language models such as CLIP are pretrained on large volumes of
internet sourced image and text pairs, and have been shown to sometimes exhibit
impressive zero- and low-shot image classification performance. However, due to
their size, fine-tuning these models on new datasets can be prohibitively
expensive, both in terms of the supervision and compute required. To combat
this, a series of light-weight adaptation methods have been proposed to
efficiently adapt such models when limited supervision is available. In this
work, we show that while effective on internet-style datasets, even those
remedies under-deliver on classification tasks with images that differ
significantly from those commonly found online. To address this issue, we
present a new approach called SVL-Adapter that combines the complementary
strengths of both vision-language pretraining and self-supervised
representation learning. We report an average classification accuracy
improvement of 10% in the low-shot setting when compared to existing methods,
on a set of challenging visual classification tasks. Further, we present a
fully automatic way of selecting an important blending hyperparameter for our
model that does not require any held-out labeled validation data. Code for our
project is available here: https://github.com/omipan/svl_adapter.
