Before revision:  One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space , learning meaningful structure by mirroring the space of linguistic representations. Contrary to this intuition, we show that a visual model (BiT-M) trained on a very large supervised image dataset (ImageNet-21k) can be as efficient for generalization ( few-shot learning, unsupervised clustering) as its multimodal counterpart (CLIP).
After revision:  One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space . Here, we systematically evaluate various multimodal architectures and vision-only models in terms of unsupervised clustering, few-shot learning, unsupervised clustering) as its multimodal counterpart (CLIP).