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

UFO$^2$: A Unified Framework towards Omni-supervised Object Detection