Does language help generalization in vision models?

Paper accepted for presentation at the ViGIL 2021 workshop @NAACL. This version: added models to the comparison (ICMLM, TSM); added tests of adversarial robustness; mistake identified and corrected in the normalization of image features; results and conclusions updated accordinglyInternational audienceVision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets. A recent model (CLIP) was found to generalize well in zero-shot and transfer learning settings. This could imply that linguistic or "semantic grounding" confers additional generalization abilities to the visual feature space. Here, we systematically evaluate various multimodal architectures and vision-only models in terms of unsupervised clustering, few-shot learning, transfer learning and adversarial robustness. In each setting, multimodal training produced no additional generalization capability compared to standard supervised visual training. We conclude that work is still required for semantic grounding to help improve vision models