Semantic segmentation of high-resolution remote sensing images (HRRSIs) is essential for various geospatial applications. While recent transformer-based models demonstrate promising global modeling capabilities, they are susceptible to attention noise—particularly in cluttered or noisy scenes—leading to degraded segmentation quality. This challenge is amplified in HRRSIs, where spatially heterogeneous noise and ambiguous object boundaries are prevalent. To address this problem, we propose PDDFormer, a Position-aware Differential Denoising Transformer tailored for semantic segmentation of HRRSIs. Central to our design is the Spatially Gated Differential Attention (SGDA), which adaptively modulates denoising strength across spatial positions using a learnable gate. Unlike prior differential attention models, SGDA introduces position-aware denoising that dynamically adjusts attention suppression at each token, guided by spatial semantics. This allows PDDFormer to suppress redundant background responses while preserving critical structural semantics. Extensive experiments on two public benchmarks, ISPRS Potsdam and LoveDA, demonstrate that PDDFormer consistently outperforms state-of-the-art methods in both accuracy and boundary preservation. Moreover, PDDFormer achieves favorable performance-efficiency trade-offs, making it practical for large-scale geospatial analysis. Visualizations further confirm the model’s ability to generate cleaner, more coherent segmentation maps, validating the effectiveness of our spatially adaptive denoising strategy.

Real-time 3-D human action recognition has broad industrial applications, such as surveillance, human–computer interaction, and healthcare monitoring. By relying on complex spatio-temporal local encoding, most existing point cloud sequence networks capture spatio-temporal local structures to recognize 3-D human actions. To simplify the point cloud sequence modeling task, we propose a lightweight and effective point cloud sequence network referred to as SequentialPointNet for real-time 3-D action recognition. Instead of capturing spatio-temporal local structures, SequentialPointNet encodes the temporal evolution of static appearances to recognize human actions. First, we define a novel type of point data, hyperpoint, to better describe the temporally changing human appearances. A theoretical foundation is provided to clarify the information equivalence property for converting point cloud sequences into hyperpoint sequences. Second, the point cloud sequence modeling task is decomposed into a hyperpoint embedding task and a hyperpoint sequence modeling task. Specifically, for hyperpoint embedding, the static point cloud technology is employed to convert point cloud sequences into hyperpoint sequences, which introduces inherent frame-level parallelism; for hyperpoint sequence modeling, a hyperpoint-mixer module is designed as the basic building block to learning the spatio-temporal features of human actions. Extensive experiments on three widely-used 3-D action recognition datasets demonstrate that the proposed SequentialPointNet achieves a competitive classification performance with up to 10× faster than existing approaches.

Skeleton-based human action recognition has emerged as a prominent research focus in computer vision, with significant progress achieved in recent years. However, existing methods often suffer substantial performance degradation under real-world data constraints, such as body occlusion, missing frames, and noise. These limitations critically undermine the robustness of related techniques in practical applications. To address these challenges, we propose a Spatial Temporal Self-compensating Graph Convolutional Network (STSc-GCN), which skillfully utilizes the systematic and regular nature of human movement to mitigate performance degradation caused by data constraints through a data self-compensation mechanism. Specifically, STSc-GCN comprises two key modules: 1) Collaborative Motion Spatial Compensation (CMSC). This module designs multiple distinct topological relationships, primarily including Walk-probability Generality Topology and Self-organizing Particularity Topology, respectively, to deeply explore the universal and personalized collaborative relationships between human joints. These relationships help compensate for the lack of information caused by spatial data constraints. 2) Meta-action Sharpening Temporal Compensation (MSTC). This module introduces a novel motion sharpening mechanism that enhances key dynamic information within the meta-action sequences through cross-attention technology, thereby improving model adaptability to missing-frame scenarios. STSc-GCN achieves state-of-the-art performance on four constrained datasets and shows superior results on three widely used standard datasets, confirming its effectiveness in both constrained and general scenarios. Code will be available at https://github.com/XingLi1012/STSc-GCN.git.