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.
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