Knowledge Combination to Learn Rotated Detection without Rotated Annotation (with Tianyu Zhu, Bryce Ferenczi, Pulak Purkait, Hamid Rezatofighi and Anton van den Hengel)

This paper shows how to train a rotated-object detector without ever having rotated bounding-box annotations. The key insight is that axis-aligned annotations from one dataset can be combined with per-pixel segmentation masks from another to bootstrap an oriented detection signal, with the rotation knowledge transferred into the final detector through a combination loss. This removes the need for expensive rotated annotation on every new domain where orientation matters, such as aerial imagery or industrial inspection.

[CVPR 2023 paper]