Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection

• URL: http://arxiv.org/abs/2411.13001v1
• Date: Wed, 20 Nov 2024 02:57:35 GMT
• Title: Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
• Authors: Xinhao Zhong, Siyu Jiao, Yao Zhao, Yunchao Wei,
• Abstract summary: In open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distribution (OOD) classes. Applying semi-supervised detectors in such settings can lead to misclassifying OOD class as ID classes. We propose a simple yet effective method, termed Collaborative Feature-Logits Detector (CFL-Detector)
• Score: 75.02249869573994
• License:
• Abstract: Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data share the same label space. However, in open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distribution (OOD) classes. Applying semi-supervised detectors in such settings can lead to misclassifying OOD class as ID classes. To alleviate this issue, we propose a simple yet effective method, termed Collaborative Feature-Logits Detector (CFL-Detector). Specifically, we introduce a feature-level clustering method using contrastive loss to clarify vector boundaries in the feature space and highlight class differences. Additionally, by optimizing the logits-level uncertainty classification loss, the model enhances its ability to effectively distinguish between ID and OOD classes. Extensive experiments demonstrate that our method achieves state-of-the-art performance compared to existing methods.

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