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iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds
Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging.
Towards Accurate Open-Set Recognition via Background-Class Regularization
To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e. g., distance-based feature analyses, or complicated network architectures.
Distance-Based Background Class Regularization for Open-Set Recognition
In this paper, we propose a novel distance-based BCR method suitable for OSR, which limits the feature space of known-class data in a class-wise manner and then makes background-class samples located far away from the limited feature space.
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