Baochen Yao

Yuting Hong, Li Dong, Xiaojie Qiu et al.

Semi-Supervised Domain Adaptation (SSDA) leverages knowledge from a fully labeled source domain to classify data in a partially labeled target domain. Due to the limited number of labeled samples in the target domain, there can be intrinsic similarity of classes in the feature space, which may result in biased predictions, even when the model is trained on a balanced dataset. To overcome this limitation, we introduce a multi-view consistency framework, which includes two views for training strongly augmented data. One is a debiasing strategy for correcting class-wise prediction probabilities according to the prediction performance of the model. The other involves leveraging pseudo-negative labels derived from the model predictions. Furthermore, we introduce a cross-domain affinity learning aimed at aligning features of the same class across different domains, thereby enhancing overall performance. Experimental results demonstrate that our method outperforms the competing methods on two standard domain adaptation datasets, DomainNet and Office-Home. Combining unsupervised domain adaptation and semi-supervised learning offers indispensable contributions to the industrial sector by enhancing model adaptability, reducing annotation costs, and improving performance.

Yuting Hong, Yongkang Wu, Hui Xiao et al.

In fine-grained road scene understanding, semantic segmentation plays a crucial role in enabling vehicles to perceive and comprehend their surroundings. By assigning a specific class label to each pixel in an image, it allows for precise identification and localization of detailed road features, which is vital for high-quality scene understanding and downstream perception tasks. A key challenge in this domain lies in improving the recognition performance of minority classes while mitigating the dominance of majority classes, which is essential for achieving balanced and robust overall performance. However, traditional semi-supervised learning methods often train models overlooking the imbalance between classes. To address this issue, firstly, we propose a general training module that learns from all the pseudo-labels without a conventional filtering strategy. Secondly, we propose a professional training module to learn specifically from reliable minority-class pseudo-labels identified by a novel mismatch score metric. The two modules are crossly supervised by each other so that it reduces model coupling which is essential for semi-supervised learning. During contrastive learning, to avoid the dominance of the majority classes in the feature space, we propose a strategy to assign evenly distributed anchors for different classes in the feature space. Experimental results on multiple public benchmarks show that our method surpasses traditional approaches in recognizing tail classes.