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Despite the superior performance brought by vision-and-language pretraining,it remains unclear whether learning with multi-modal data can help understandeach individual modality. In this work, we investigate how language can helpwith visual representation learning from a probing perspective. Specifically,we compare vision-and-language and vision-only models by probing their visualrepresentations on a broad range of tasks, in order to assess the quality ofthe learned representations in a fine-grained manner. Interestingly, ourprobing results suggest that vision-and-language models are better at labelprediction tasks like object and attribute prediction, while vision-only modelsare stronger at dense prediction tasks that require more localized information.With further analysis using detailed metrics, our study suggests that languagehelps vision models learn better semantics, but not localization. Code isreleased at https://github.com/Lizw14/visual_probing.

Localization vs. Semantics: How Can Language Benefit Visual Representation Learning?