Credible Teacher for Semi-Supervised Object Detection in Open Scene

• URL: http://arxiv.org/abs/2401.00695v2
• Date: Wed, 3 Jan 2024 02:33:49 GMT
• Title: Credible Teacher for Semi-Supervised Object Detection in Open Scene
• Authors: Jingyu Zhuang, Kuo Wang, Liang Lin, Guanbin Li
• Abstract summary: In Open Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may contain unknown objects not observed in the labeled data. It is detrimental to the current methods that mainly rely on self-training, as more uncertainty leads to the lower localization and classification precision of pseudo labels. We propose Credible Teacher, an end-to-end framework to prevent uncertain pseudo labels from misleading the model.
• Score: 106.25850299007674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-Supervised Object Detection (SSOD) has achieved resounding success by leveraging unlabeled data to improve detection performance. However, in Open Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may contains unknown objects not observed in the labeled data, which will increase uncertainty in the model's predictions for known objects. It is detrimental to the current methods that mainly rely on self-training, as more uncertainty leads to the lower localization and classification precision of pseudo labels. To this end, we propose Credible Teacher, an end-to-end framework. Credible Teacher adopts an interactive teaching mechanism using flexible labels to prevent uncertain pseudo labels from misleading the model and gradually reduces its uncertainty through the guidance of other credible pseudo labels. Empirical results have demonstrated our method effectively restrains the adverse effect caused by O-SSOD and significantly outperforms existing counterparts.

Related papers

• Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection [75.02249869573994]
  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)
  arXiv  Detail & Related papers  (2024-11-20T02:57:35Z)
• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning [24.733773208117363]
  We propose Uncertainty-Aware Teacher Learning to reduce the number of incorrect pseudo labels in the self-training stage. We also propose Student-Student Collaborative Learning that allows the transfer of reliable labels between two student networks. We evaluate our proposed method on five DS-NER datasets, demonstrating that our method is superior to the state-of-the-art DS-NER methods.
  arXiv  Detail & Related papers  (2023-11-14T09:09:58Z)
• Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant [72.4512562104361]
  We argue that the unlabeled data with pseudo labels can facilitate the learning of representative features in the feature extractor. Motivated by this consideration, we propose a novel framework, Gentle Teaching Assistant (GTA-Seg) to disentangle the effects of pseudo labels on feature extractor and mask predictor.
  arXiv  Detail & Related papers  (2023-01-18T07:11:24Z)
• Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty [46.21528260727673]
  We propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework. By jointly filtering the pseudo-labels for the classification and regression heads, the student network receives better guidance from the teacher network for object detection task.
  arXiv  Detail & Related papers  (2022-12-06T04:37:51Z)
• Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection [6.345037597566315]
  Pseudo-label based self training approaches are a popular method for source-free unsupervised domain adaptation. We propose an uncertainty-aware mean teacher framework which implicitly filters incorrect pseudo-labels during training.
  arXiv  Detail & Related papers  (2021-09-29T18:17:09Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.