Existing work on object detection often relies on a single form of
annotation: the model is trained using either accurate yet costly bounding
boxes or cheaper but less expressive image-level tags. However, real-world
annotations are often diverse in form, which challenges these existing works.
In this paper, we present UFO$^2$, a unified object detection framework that
can handle different forms of supervision simultaneously. Specifically, UFO$^2$
incorporates strong supervision (e.g., boxes), various forms of partial
supervision (e.g., class tags, points, and scribbles), and unlabeled data.
Through rigorous evaluations, we demonstrate that each form of label can be
utilized to either train a model from scratch or to further improve a
pre-trained model. We also use UFO$^2$ to investigate budget-aware
omni-supervised learning, i.e., various annotation policies are studied under a
fixed annotation budget: we show that competitive performance needs no strong
labels for all data. Finally, we demonstrate the generalization of UFO$^2$,
detecting more than 1,000 different objects without bounding box annotations.

本文提出了一种名为 UFO$^2$ 的统一目标检测框架，可以同时处理不同形式的监督学习，包括强监督（如 bounding boxes）和各种形式的部分监督学习（如类别标签、点、涂鸦）和未标记数据。作者通过严格的评估表明，每种形式的标签可以用于从头开始训练模型或进一步改善预训练模型，同时在固定的注释预算下研究各种注释策略，在不需要强标签的情况下可以实现有竞争力的性能。最后，作者还证明了 UFO$^2$ 的泛化能力，可以检测超过 1,000 种不同的物体，而无需 bounding box 标注。