Computer Vision Models That Learn From Language

Any typical successful computer vision model first undergoes pre-training on ImageNet and then proceeds to do the tasks such as classification or captioning of the image. But can the vision models learn more from language? To explore this, two researchers from the University Of Michigan introduced "VirTex", a pretraining approach to learn visual features via language using fewer images. The aim of this work is to demonstrate that natural language can provide supervision for learning transferable visual representations with better data-efficiency than other approaches. Introducing "VirTex": a pretraining approach to learn visual features via language using fewer images.