  Large-scale contrastive vision-language pre-training has shown significant
progress in visual representation learning. Unlike traditional visual systems
trained by a fixed set of discrete labels, a new paradigm was introduced in
\cite{radford2021learning} to directly learn to align images with raw texts in
an open-vocabulary setting. On downstream tasks, a carefully chosen text prompt
is employed to make zero-shot predictions.~To avoid non-trivial prompt
engineering, context optimization \cite{zhou2021coop} has been proposed to
learn continuous vectors as task-specific prompts with few-shot training
examples.~In this paper, we show that there is an alternative path to achieve
better vision-language models other than prompt tuning.~While prompt tuning is
for the textual inputs, we propose CLIP-Adapter to conduct fine-tuning with
feature adapters on either visual or language branch. Specifically,
CLIP-Adapter adopts an additional bottleneck layer to learn new features and
performs residual-style feature blending with the original pre-trained
features.~As a consequence, CLIP-Adapter is able to outperform context
optimization while maintains a simple design. Experiments and extensive
ablation studies on various visual classification tasks demonstrate the
effectiveness of our approach.
