Language-AugmentedVisualModels

Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we buildELEVATER 1, the first benchmark and toolkit for evaluating (pre-trained) language-augmented visual models. ELEVATERis composed of three components.