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Better object detection by filtering extra unlabeled data with RUPL

Better object detection by filtering extra unlabeled data with RUPL


Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty

arXiv paper abstract https://arxiv.org/abs/2212.02747



Semi-supervised object detection (SSOD) aims to boost detection performance by leveraging extra unlabeled data.


The teacher-student framework has been shown to be promising for SSOD, in which a teacher network generates pseudo-labels for unlabeled data to assist the training of a student network. Since the pseudo-labels are noisy, filtering the pseudo-labels is crucial


... propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework.


For the classification head, OCL (Object-wise Contrastive Learning) regularizes the object representation learning that utilizes unlabeled data to improve pseudo-label filtering by enhancing the discriminativeness of the classification score.


... For the regression head, ... further propose RUPL (Regression-Uncertainty-guided Pseudo-Labeling) to learn the aleatoric uncertainty of object localization for label filtering.


... demonstrate the superiority of ... proposed method with competitive performance compared to existing methods.



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