解决开放集半监督目标检测中的误判问题，提升模型对未知类的识别能力。
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
- 通过特征级对比学习明确类别边界，增强特征空间区分性。
- 引入置信度不确定性损失，有效区分已知类与未知类。
- 在开放集场景下性能领先，适合实际应用中存在未知类的检测任务。
当前半监督目标检测方法通过利用大量未标记数据提升检测性能，假设已标记与未标记数据共享相同标签空间。但在开放集场景下，未标记数据包含分布内（ID）和分布外（OOD）类别。直接应用半监督检测器可能导致将OOD类别误判为ID类别。为此，我们提出一种简单而有效的方法——协同特征-逻辑值对比检测器（CFL-Detector）。具体而言，采用基于对比损失的特征级聚类方法，清晰划分特征空间中的向量边界，突出类别差异；同时，通过优化逻辑值级不确定性分类损失，增强模型区分ID与OOD类别的能力。大量实验表明，该方法在现有方法中达到最优性能。
原文摘要 · Abstract (English)
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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