  Recent progress in the few-shot adaptation of Vision-Language Models (VLMs)
has further pushed their generalization capabilities, at the expense of just a
few labeled samples within the target downstream task. However, this promising,
already quite abundant few-shot literature has focused principally on prompt
learning and, to a lesser extent, on adapters, overlooking the recent advances
in Parameter-Efficient Fine-Tuning (PEFT). Furthermore, existing few-shot
learning methods for VLMs often rely on heavy training procedures and/or
carefully chosen, task-specific hyper-parameters, which might impede their
applicability. In response, we introduce Low-Rank Adaptation (LoRA) in few-shot
learning for VLMs, and show its potential on 11 datasets, in comparison to
current state-of-the-art prompt- and adapter-based approaches. Surprisingly,
our simple CLIP-LoRA method exhibits substantial improvements, while reducing
the training times and keeping the same hyper-parameters in all the target
tasks, i.e., across all the datasets and numbers of shots. Certainly, our
surprising results do not dismiss the potential of prompt-learning and
adapter-based research. However, we believe that our strong baseline could be
used to evaluate progress in these emergent subjects in few-shot VLMs.
