An Active Semi-Supervised Learning for Object Detection

The rapid progress in deep learning technology has rendered extensive annotated datasets indispensable for augmenting algorithmic performance. However, annotating datasets requires a significant amount of resources and manpower. To address the high costs of annotating object detection tasks, we pres...

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Bibliographic Details
Published in:2023 International Conference on Culture-Oriented Science and Technology (CoST) pp. 257 - 261
Main Authors: Zhao, Yu, Yang, Yingyun, Chen, Sijin
Format: Conference Proceeding
Language:English
Published: IEEE 11.10.2023
Subjects:
active learning strategy
Adaptation models
Costs
Deep learning
Focal loss
Object detection
Resource management
Semantics
semi-supervised learning algorithm
Semisupervised learning
SimOTA
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Summary:The rapid progress in deep learning technology has rendered extensive annotated datasets indispensable for augmenting algorithmic performance. However, annotating datasets requires a significant amount of resources and manpower. To address the high costs of annotating object detection tasks, we present an active semi-supervised learning algorithm framework tailored for object detection. The framework utilizes a semi-supervised learning model equipped with active learning strategies to manually label difficult-to-process minority samples. Additionally, we introduce the SimOTA(Simplified Optimal Transport Assignment) label assignment strategy, which obtains optimal samples from a global perspective. Moreover, the loss function for the unlabeled data has been adjusted to enable the utilization of semantic information from the noisy pseudo labels. The empirical findings obtained from assessments conducted on publicly accessible datasets provide evidence that the active semi-supervised learning algorithm framework proposed in this paper outperforms current advanced active learning strategies and semi-supervised learning algorithms in the area of object detection.
DOI:10.1109/CoST60524.2023.00059