Current uses of tagged images typically exploit only the most explicit information: the link between the nouns named and the objects present somewhere in the image. We propose to leverage "unspoken" cues that rest within an ordered list of image tags so as to improve object localization. We define three novel implicit features from an image's tags-the relative prominence of each object as signified by its order of mention, the scale constraints implied by unnamed objects, and the loose spatial links hinted at by the proximity of names on the list. By learning a conditional density over the localization parameters (position and scale) given these cues, we show how to improve both accuracy and efficiency when detecting the tagged objects. Furthermore, we show how the localization density can be learned in a semantic space shared by the visual and tag-based features, which makes the technique applicable for detection in untagged input images. We validate our approach on the PASCAL VOC, LabelMe, and Flickr image data sets, and demonstrate its effectiveness relative to both traditional sliding windows as well as a visual context baseline. Our algorithm improves state-of-the-art methods, successfully translating insights about human viewing behavior (such as attention, perceived importance, or gaze) into enhanced object detection.