  In this paper, we focus on training an open-set object detector under the
condition of scarce training samples, which should distinguish the known and
unknown categories. Under this challenging scenario, the decision boundaries of
unknowns are difficult to learn and often ambiguous. To mitigate this issue, we
develop a novel open-set object detection framework, which delves into
conditional evidence decoupling for the unknown rejection. Specifically, we
select pseudo-unknown samples by leveraging the discrepancy in attribution
gradients between known and unknown classes, alleviating the inadequate unknown
distribution coverage of training data. Subsequently, we propose a Conditional
Evidence Decoupling Loss (CEDL) based on Evidential Deep Learning (EDL) theory,
which decouples known and unknown properties in pseudo-unknown samples to learn
distinct knowledge, enhancing separability between knowns and unknowns.
Additionally, we propose an Abnormality Calibration Loss (ACL), which serves as
a regularization term to adjust the output probability distribution,
establishing robust decision boundaries for the unknown rejection. Our method
has achieved the superiority performance over previous state-of-the-art
approaches, improving the mean recall of unknown class by 7.24% across all
shots in VOC10-5-5 dataset settings and 1.38% in VOC-COCO dataset settings. The
code is available via https://github.com/zjzwzw/CED-FOOD.
