Towards Open Vocabulary Object Detection without Human-provided Bounding Boxes

An open vocabulary detection framework that can be trained without manually provided bounding-box annotations is proposed that outperforms the state-of-the-arts (SOTA) that are trained using human annotatedbounding-boxes by 3% AP on COCO novel categories even though the training source is not equipped with manual bounded-box labels.