FSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training
School of Automation Science and Engineering, Xi’an Jiaotong University（自动化科学与工程学院，西安交通大学）；Institute for Low-Altitude Regulation, School of Electronics and Information Engineering, Xi’an Jiaotong University（低空调控研究所，电子与信息工程学院，西安交通大学）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人工智能与机器人研究所，西安交通大学）
Crowd counting plays a vital role in public safety, traffic regulation, and smart city management. However, despite the impressive progress achieved by CNN- and Transformer-based models, their performance often deteriorates when applied across diverse environments due to severe domain discrepancies. Direct joint training on multiple datasets, which intuitively should enhance generalization, instead results in negative transfer, as shared and domain-specific representations become entangled. To address this challenge, we propose the Feature Separation and Cross-Attention Network FSCA-Net, a unified framework that explicitly disentangles feature representations into domain-invariant and domain-specific components. A novel cross-attention fusion module adaptively models interactions between these components, ensuring effective knowledge transfer while preserving dataset-specific discriminability. Furthermore, a mutual information optimization objective is introduced to maximize consistency among domain-invariant features and minimize redundancy among domain-specific ones, promoting complementary shared-private representations. Extensive experiments on multiple crowd counting benchmarks demonstrate that FSCA-Net effectively mitigates negative transfer and achieves state-of-the-art cross-dataset generalization, providing a robust and scalable solution for real-world crowd analysis.
Unsupervised Contrastive Learning Using Out-Of-Distribution Data for Long-Tailed Dataset
Department of Electronic Engineering, Seoul National University of Science and Technology（电子工程系，首尔科学技术大学）；Department of Electrical and Information Engineering, Seoul National University of Science and Technology（电气与信息工程系，首尔科学技术大学）；School of Electrical and Electronics Engineering, Chung-Ang University（电气电子工程学院， Chung-Ang 大学）
Computational optimization of convolutional neural networks using separated filters architecture
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Smart Engines Ltd.（Smart Engines公司）；Institute For Information Transmission Problems (Kharkevich Institute)（信息传输问题研究所（哈尔凯维奇研究所））
Row-Column Separated Attention Based Low-Light Image/Video Enhancement
School of Artificial Intelligence, Jilin University, China（吉林大学人工智能学院）；College of Software, Jilin University, China（吉林大学软件学院）；Institute of Computing Technology, Chinese Academy of Sciences, China（中国科学院计算技术研究所）
Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface
Faculty of Information Engineering, Kanazawa Gakuin University（信息工程学部，金泽神户大学）
