Pseudo-unknown uncertainty learning for open set object detection

Abstract

Despite the significant strides made by modern object detectors in the closed-set scenarios, open-set object detection (OSOD) remains a formidable challenge. This is particularly evident in misclassifying objects from unknown categories into pre-existing known classes or ignored background classes. A novel approach called PUDet (Pseudo-unknown Uncertainty Detector) based on Evidential Deep Learning (EDL) is proposed, incorporating two modules: the Class-wise Contrastive Learning Network (CCL) and the Uncertainty-Aware Labeling Network (UAL). For CCL, the module leverages class-wise contrastive learning to encourage intra-class compactness and inter-class separation, thereby reducing the overlap between known and unknown classes. Simultaneously, it establishes compact boundaries for known classes and generates pseudo-unknown candidates to facilitate UAL for better learning pseudo-unknown uncertainty. For UAL, the Weight-Impact EDL (WI-EDL) approach is introduced to enhance uncertainty in edge samples by collecting categorical evidence and weight impact. Subsequently, UAL refines uncertainty via localization quality calibration, facilitating the mining of pseudo-unknown samples from foreground and background proposals to construct compact boundaries between known and unknown categories. In comparison to the state of the arts, the proposed PUDet showcases a substantial improvement, achieving a reduction in Absolute Open-Set Errors by 13%–16% across six OSOD benchmarks.