The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data is gaining attention for its improved classification accuracy. However, effectively integrating the rich spectral information of HSI and the elevation features of LiDAR has remained a challenge in multimodal fusion. This article proposes a novel approach called progressive semantic enhancement network

Owing to the normalized instance scales and robust supervision, heatmap-based human pose estimation (HPE) methods with top-down paradigm have achieved a dominant performance. However, there are two inherent deviations in the basic framework, i.e., representation and target deviations, resulting in performance bottlenecks. The representation deviation is caused by transforming various scales of instances

Heterogeneity is a fundamental characteristic of brain diseases, distinguished by variability not only in brain atrophy but also in the complexity of neural connectivity and brain networks. However, existing data-driven methods fail to provide a comprehensive analysis of brain heterogeneity. Recently, dynamic neural networks (DNNs) have shown significant advantages in capturing sample-wise heterogeneity

Support vector machines (SVMs) have been the learning model of choice in numerous classification applications. While SVMs are widely successful in real-world deployments, they remain susceptible to mislabeled examples in training datasets where the presence of few faults can severely affect decision boundaries, thereby affecting the model's performance on unseen data. In this brief, we develop and

Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encountering the dynamic cluster imbalance issue. That is, the imbalance ratio (IR) of clusters changes over time, which can easily lead to fluctuations in either the accuracy or the efficiency of streaming data clustering. Therefore

The deep reinforcement learning (DRL) has made significant progress in various simulation environments. However, applying DRL methods to real-world scenarios poses certain challenges due to limitations in visual fidelity, scene complexity, and task diversity within existing environments. To address limitations and explore the potential ability of DRL, we developed a 3-D open-world first-person shooter