Combining multiple datasets enables performance boost on many computer vision
tasks. But similar trend has not been witnessed in object detection when
combining multiple datasets due to two inconsistencies among detection
datasets: taxonomy difference and domain gap. In this paper, we address these
challenges by a new design (named Detection Hub) that is dataset-aware and
category-aligned. It not only mitigates the dataset inconsistency but also
provides coherent guidance for the detector to learn across multiple datasets.
In particular, the dataset-aware design is achieved by learning a dataset
embedding that is used to adapt object queries as well as convolutional kernels
in detection heads. The categories across datasets are semantically aligned
into a unified space by replacing one-hot category representations with word
embedding 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 achieves
significant performance gains over training on each dataset alone. Detection
Hub further achieves SoTA performance on UODB benchmark with wide variety of
datasets.

本研究提出一种新的设计（称为 Detection Hub），它是数据集感知并具有类别对齐功能，通过学习一种数据集嵌入来自适应对象查询以及检测头中的卷积核，从而既减轻了对目标检测的数据集不一致性，又提供了一致的指导，以便检测器跨多个数据集学习。实验表明，检测中心进一步在各种数据集的 UODB 基准测试中实现了 SoTA 性能。