Online Open-set Semi-supervised Object Detection with Dual Competing Head
arxiv(2023)
摘要
Open-set semi-supervised object detection (OSSOD) task leverages practical
open-set unlabeled datasets that comprise both in-distribution (ID) and
out-of-distribution (OOD) instances for conducting semi-supervised object
detection (SSOD). The main challenge in OSSOD is distinguishing and filtering
the OOD instances (i.e., outliers) during pseudo-labeling since OODs will
affect the performance. The only OSSOD work employs an additional offline OOD
detection network trained solely with labeled data to solve this problem.
However, the limited labeled data restricts the potential for improvement.
Meanwhile, the offline strategy results in low efficiency. To alleviate these
issues, this paper proposes an end-to-end online OSSOD framework that improves
performance and efficiency: 1) We propose a semi-supervised outlier filtering
method that more effectively filters the OOD instances using both labeled and
unlabeled data. 2) We propose a threshold-free Dual Competing OOD head that
further improves the performance by suppressing the error accumulation during
semi-supervised outlier filtering. 3) Our proposed method is an online
end-to-end trainable OSSOD framework. Experimental results show that our method
achieves state-of-the-art performance on several OSSOD benchmarks compared to
existing methods. Moreover, additional experiments show that our method is more
efficient and can be easily applied to different SSOD frameworks to boost their
performance.
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