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Combining multiple datasets enables performance boost on many computer visiontasks. But similar trend has not been witnessed in object detection whencombining multiple datasets due to two inconsistencies among detectiondatasets: taxonomy difference and domain gap. In this paper, we address thesechallenges by a new design (named Detection Hub) that is dataset-aware andcategory-aligned. It not only mitigates the dataset inconsistency but alsoprovides coherent guidance for the detector to learn across multiple datasets.In particular, the dataset-aware design is achieved by learning a datasetembedding that is used to adapt object queries as well as convolutional kernelsin detection heads. The categories across datasets are semantically alignedinto a unified space by replacing one-hot category representations with wordembedding and leveraging the semantic coherence of language embedding.Detection Hub fulfills the benefits of large data on object detection.Experiments demonstrate that joint training on multiple datasets achievessignificant performance gains over training on each dataset alone. DetectionHub further achieves SoTA performance on UODB benchmark with wide variety ofdatasets.

Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding