  Existing object detection methods are bounded in a fixed-set vocabulary by
costly labeled data. When dealing with novel categories, the model has to be
retrained with more bounding box annotations. Natural language supervision is
an attractive alternative for its annotation-free attributes and broader object
concepts. However, learning open-vocabulary object detection from language is
challenging since image-text pairs do not contain fine-grained object-language
alignments. Previous solutions rely on either expensive grounding annotations
or distilling classification-oriented vision models. In this paper, we propose
a novel open-vocabulary object detection framework directly learning from
image-text pair data. We formulate object-language alignment as a set matching
problem between a set of image region features and a set of word embeddings. It
enables us to train an open-vocabulary object detector on image-text pairs in a
much simple and effective way. Extensive experiments on two benchmark datasets,
COCO and LVIS, demonstrate our superior performance over the competing
approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask
mAP on LVIS. Code is available at: https://github.com/clin1223/VLDet.
