In this paper, we introduce a novel approach to automatically assign entity labels to images from existing noisy image-text pairs. The approach employees a named entity recognition model to extract entities from text, and uses a CLIP model to select the right entities as the labels of the paired image. The approach is simple, and can be readily scaled up to billions of image-text pairs mined from the web, through which we have successfully created a dataset with 2 millions of distinct entities. We study new training approaches on the collected new dataset with large scale entity labels… In this paper, we introduce a novel approach to automatically assign entity labels to images from existing noisy image-text pairs. The approach employees a named entity recognition model to extract entities from text, and uses a CLIP model to select the right entities as the labels of the paired image. The approach is simple, and can be readily scaled up to billions of image-text pairs mined from the web, through which we have successfully created a dataset with 2 millions of distinct entities. We study new training approaches on the collected new dataset with large scale entity labels… Read More