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To alleviate the cost of obtaining accurate bounding boxes for trainingtoday's state-of-the-art object detection models, recent weakly superviseddetection work has proposed techniques to learn from image-level labels.However, requiring discrete image-level labels is both restrictive andsuboptimal. Real-world "supervision" usually consists of more unstructuredtext, such as captions. In this work we learn association maps between imagesand captions. We then use a novel objectness criterion to rank the resultingcandidate boxes, such that high-ranking boxes have strong gradients along alledges. Thus, we can detect objects beyond a fixed object category vocabulary,if those objects are frequent and distinctive enough. We show that ourobjectness criterion improves the proposed bounding boxes in relation to priorweakly supervised detection methods. Further, we show encouraging results onobject detection from image-level captions only.

Learning to discover and localize visual objects with open vocabulary