Before revision: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot learning). 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.
After revision: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot and transfer learning settings. This could imply that linguistic or "semantic grounding" of the visual feature space , learning meaningful structure by mirroring the space of linguistic representations.