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Despite great progress in object detection, most existing methods work onlyon a limited set of object categories, due to the tremendous human effortneeded for bounding-box annotations of training data. To alleviate the problem,recent open vocabulary and zero-shot detection methods attempt to detect novelobject categories beyond those seen during training. They achieve this goal bytraining on a pre-defined base categories to induce generalization to novelobjects. However, their potential is still constrained by the small set of basecategories available for training. To enlarge the set of base classes, wepropose a method to automatically generate pseudo bounding-box annotations ofdiverse objects from large-scale image-caption pairs. Our method leverages thelocalization ability of pre-trained vision-language models to generate pseudobounding-box labels and then directly uses them for training object detectors.Experimental results show that our method outperforms the state-of-the-art openvocabulary detector by 8% AP on COCO novel categories, by 6.3% AP on PASCALVOC, by 2.3% AP on Objects365 and by 2.8% AP on LVIS. Code is available athttps://github.com/salesforce/PB-OVD.

Open Vocabulary Object Detection with Pseudo Bounding-Box Labels