Self-training is widely used in unsupervised domain adaptation (UDA) by assigning pseudo labels to unlabeled samples. However, existing self-training strategies bring bias, while potentially inaccurate pseudo labels may accumulate errors during self-training (self-training shift) and the inability to accurately distinguish features may bring prediction bias (class shift). To address these issues, we propose Shift-Driven Learning (SDL). First, we decouple the generation and utilization of pseudo labels to mitigate the direct error accumulation. Second, we measure the maximum training shift of data, where the classifier achieves high accuracy on labeled data while making as many mistakes as possible on unlabeled data. Then we adversarially optimize the feature representations generation to indirectly decrease the self-training shift. Third, we minimize the class shift by data rearrangement strategy and joint contrastive learning, which find class-level discriminative feature representations. Extensive experiments justify that SDL outperforms SOTA methods on three UDA datasets with considerable gains.

Semantic Scene Completion (SSC) aims to generate a complete semantic scene from an incomplete input. Existing approaches often employ dense network architectures with a high parameter count, leading to increased model complexity and resource demands. To address these limitations, we propose RWKV-PCSSC, a lightweight point cloud semantic scene completion network inspired by the Receptance Weighted Key Value (RWKV) mechanism. Specifically, we introduce a RWKV Seed Generator (RWKV-SG) module that can aggregate features from a partial point cloud to produce a coarse point cloud with coarse features. Subsequently, the point-wise feature of the point cloud is progressively restored through multiple stages of the RWKV Point Deconvolution (RWKV-PD) modules. By leveraging a compact and efficient design, our method achieves a lightweight model representation. Experimental results demonstrate that RWKV-PCSSC reduces the parameter count by 4.18× and improves memory efficiency by 1.37× compared to state-of-the-art methods PointSSC[51]. Furthermore, our network achieves state-of-the-art performance on established indoor (SSC-PC, NYUCAD-PC) and outdoor (PointSSC) scene dataset, as well as on our proposed datasets (NYUCAD-PC-V2, 3D-FRONT-PC).

While domain adaptation (DA) methods have made significant strides in remote sensing community, most current works assume that the source domain labels are accurate. However, limited emphasis has been placed on the scenario where source data are mislabeled with noisy annotations, which is more common in real applications and referred to as noisy DA (NDA). This article formulates remote sensing cross-scene classification on NDA scenarios and proposes a novel network called bilateral adaptation network (BAN), which consists of two parts: 1) forward learning (FL), which utilizes a model learning from the noisy source domain and transfers knowledge to target domain; and 2) backward learning (BL), which utilizes a dual model to acquire knowledge from the target domain and transfer it to source domain. We conduct two parts alternately and adopt a symmetrical Kullback-Leibler (KL) loss to align predictions of the model and its dual model in the same domain. This interactive strategy is able to explore bilateral relationships between domains, implicitly reducing label noise in the source domain. In addition, BAN could serve as a universal paradigm to not only improve the existing NDA methods but also enhance recent DA approaches. Comprehensive evaluations on three publicly available RGB-band remote sensing datasets and two hyperspectral datasets validate the superior effectiveness of our proposed BAN. BAN improves the average accuracy by 6.70%–15.70% on RGB datasets and overall accuracy (OA) by 1.36%–3.14% on hyperspectral datasets with flip-20% noise compared to other state-of-the-art DA and NDA approaches. Promising results indicate the potential of our approach in tackling more general and practical problems with noisy source domain.