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The use of large-scale vision-language datasets is limited for objectdetection due to the negative impact of label noise on localization. Priormethods have shown how such large-scale datasets can be used for pretraining,which can provide initial signal for localization, but is insufficient withoutclean bounding-box data for at least some categories. We propose a technique to"vet" labels extracted from noisy captions, and use them for weakly-supervisedobject detection (WSOD). We conduct analysis of the types of label noise incaptions, and train a classifier that predicts if an extracted label isactually present in the image or not. Our classifier generalizes across datasetboundaries and across categories. We compare the classifier to eleven baselineson five datasets, and demonstrate that it can improve WSOD without labelvetting by 30% (31.2 to 40.5 mAP when evaluated on PASCAL VOC)

VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection