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Jinhao Du

2 accepted papers

2025

Open-World Objectness Modeling Unifies Novel Object Detection

CVPR 2025poster

The challenge in open-world object detection, similarly to few- and zero-shot learning, is to generalize beyond the class distribution of the training data. In this paper, we propose a general class-agnostic objectness measure to limit bias toward labeled samples. One issue in open-world detection…

Cited by 1SourcePDFScholar
2023

s-Adaptive Decoupled Prototype for Few-Shot Object Detection

ICCV 2023poster

Meta-learning-based few-shot detectors use one K-average-pooled prototype (averaging along K-shot dimension) in both Region Proposal Network (RPN) and Detection head (DH) for query detection. Such plain operation would harm the FSOD performance in two aspects: 1) the poor quality of the prototype, a…

Cited by 14PDFScholar