Black-box Domain Adaptation (BDA) utilizes a black-box predictor of the source domain to label target domain data, addressing privacy concerns in Unsupervised Domain Adaptation (UDA). However, BDA assumes identical label sets across domains, which is unrealistic. To overcome this limitation, we propose a study on BDA with unknown classes in the target domain. It uses a black-box predictor to label target data and identify "unknown" categories, without requiring access to source domain data or predictor parameters, thus addressing both data privacy and category shift issues in traditional UDA. Existing methods face two main challenges: (i) Noisy pseudo-labels in knowledge distillation (KD) accumulate prediction errors, and (ii) relying on a preset threshold fails to adapt to varying category shifts. To address these, we propose ADU, a framework that allows the target domain to autonomously learn pseudo-labels guided by quality and use an adaptive threshold to identify "unknown" categories. Specifically, ADU consists of Selective Amplification Knowledge Distillation (SAKD) and Entropy-Driven Label Differentiation (EDLD). SAKD improves KD by focusing on high-quality pseudo-labels, mitigating the impact of noisy labels. EDLD categorizes pseudo-labels by quality and applies tailored training strategies to distinguish "unknown" categories, improving detection accuracy and adaptability. Extensive experiments show that ADU achieves state-of-the-art results, outperforming the best existing method by 3.1% on VisDA in the OPBDA scenario.

Concerns about privacy and the centralized collection of sensitive data have led to the development of Federated Learning, a paradigm enabling collaborative model training without the need to aggregate raw data centrally. However, variations in data distributions between source and target clients, a phenomenon known as domain shift, often lead to degraded model performance. While recent advancements in Federated Domain Generalization address this challenge, they typically operate under a closed-set assumption, disregarding scenarios where target domains introduce entirely new classes, referred to as category shift. This oversight can result in critical misclassifications in real-world applications. To overcome these limitations, we explore Federated Open-Set Domain Generalization (FedOSDG) setting for the first time, which not only preserves data privacy but also identifies new, unseen classes in the unseen target domains. Specifically, we propose the Adaptive Adjustment Boundary and Weights (AABAW) framework, comprising Stronger Classification Boundary (SCB) and Adaptive Adjustment of Weights (AAW). The SCB module reinforces the decision boundaries of the binary classifiers to handle category shift while the AAW module leverages local model diversity to increase the variance of global model, thereby enhancing the model’s generalization under domain shifts. Experimental results show that our proposed AABAW achieves state-of-the-art performance in recognizing unknown classes and H-scores in the FedOSDG task with considerable gains and maintains competitive performance in all classes.

In response to the growing demands of real-world applications, models must be capable of learning continuously under inconsistent data distribution. However, existing Class-Incremental (CI) methods fail to alleviate domain shifts, while traditional Unsupervised Domain Adaptation (UDA) techniques suffer from catastrophic forgetting and privacy concerns. To address these limitations, we explore Source-Free Class Incremental Domain Adaptation (SFCIDA) and propose a novel approach, Quantifying Samples with Invariance (QSI), for this scenario. Our proposed method involves two main strategies: (1) Semantic Restructuring. We identify confusing source category pairs and restructure images to create a negative dataset that is semantically similar to the source features, refining accurate decision boundary among source categories. (2) Invariance Quantification. The sample's confidence is then quantified by its spatial location under the special data distribution, reflecting the trade-off between invariant features and domain shifts. Guided by such strategy, samples' confidence is accumulated for the target model to prioritize reliable categories, not only mitigating the poor performance of experience replay in unsupervised scenarios, but alleviating distribution discrepancies simultaneously. Experiments demonstrate that our approach outperforms previous methods, establishing new state-of-the-art performance on the Office-31, Office-Home and DomainNet-126 datasets, with average accuracy improvements of over 7.3%, 4.9% and 10.2% respectively.

Various remote sensing applications have widely used domain adaptation (DA) methods. Since it does not need to add human interpretation in the target domain, it can be used in cross-region, multi-temporal, and multi-sensor application scenarios. In order to further optimize the design of the loss function and better address the challenges of DA in remote sensing, in this paper, we propose a new universal DA method named C3DA for scene recognition of remote sensing images. It has a comprehensive C3criterion for recognizing the "unknown" classes by innovatively fusing confidence, consistency, and certainty of samples to make our network training more efficient. We evaluate the performance of our proposed method based on six transfer tasks on three remote sensing datasets. The evaluation results show that our proposed method achieves an average H-score of 58.44%, significantly higher than other SOTA universal DA methods with an average improvement of 2.32~29.43%. Compared to the baseline ResNet-50, it achieves up to 19.92% improvement, demonstrating that the proposed method outperforms in the universal DA scenario. In the future, we also plan to expand the application of this method to more scenarios.