We consider the problem of omni-supervised object detection, which can use
unlabeled, fully labeled and weakly labeled annotations, such as image tags,
counts, points, etc., for object detection. This is enabled by a unified
architecture, Omni-DETR, based on the recent progress on student-teacher
framework and end-to-end transformer based object detection. Under this unified
architecture, different types of weak labels can be leveraged to generate
accurate pseudo labels, by a bipartite matching based filtering mechanism, for
the model to learn. In the experiments, Omni-DETR has achieved state-of-the-art
results on multiple datasets and settings. And we have found that weak
annotations can help to improve detection performance and a mixture of them can
achieve a better trade-off between annotation cost and accuracy than the
standard complete annotation. These findings could encourage larger object
detection datasets with mixture annotations. The code is available at
this https URL

研究使用统一结构 Omni-DETR 进行 omni-supervised 物体检测，将弱标注转化为准确的伪标注用于学习，取得了多个数据集的最优结果。