Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
This addresses a practical issue in semi-supervised object detection for real-world applications where data includes unknown classes, though it is incremental as it builds on existing methods.
The paper tackles the problem of open-set semi-supervised object detection, where unlabeled data includes out-of-distribution classes that can be misclassified, by proposing a collaborative feature-logits contrastive learning method that achieves state-of-the-art performance in experiments.
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.