Random Boxes Are Open-world Object Detectors
Yanghao Wang Zhongqi Yue Xian-Sheng Hua Hanwang Zhang
We show that classifiers trained with random region proposals achieve
state-of-the-art Open-world Object Detection (OWOD): they can not only maintain
the accuracy of the known objects (w/ training labels), but also considerably
improve the recall of unknown ones (w/o training labels). Specifically, we
propose RandBox, a Fast R-CNN based architecture trained on random proposals at
each training iteration, surpassing existing Faster R-CNN and Transformer based
OWOD. Its effectiveness stems from the following two benefits introduced by
randomness. First, as the randomization is independent of the distribution of
the limited known objects, the random proposals become the instrumental
variable that prevents the training from being confounded by the known objects.
Second, the unbiased training encourages more proposal explorations by using
our proposed matching score that does not penalize the random proposals whose
prediction scores do not match the known objects. On two benchmarks:
Pascal-VOC/MS-COCO and LVIS, RandBox significantly outperforms the previous
state-of-the-art in all metrics. We also detail the ablations on randomization
and loss designs. Codes are available at https://github.com/scuwyh2000/RandBox.