AI Research
Advancing the frontiers of artificial intelligence through innovative research and cutting-edge methodologies.
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Multi-modal Learning
Pioneering cross-modal understanding and representation learning for complex real-world applications.
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Computer Vision Innovation
Developing robust and interpretable computer vision systems for diverse industrial applications.
View PublicationsResearch Highlights
Noisy Correspondence
Noisy correspondence learning emerges as a new research DIRECTION of noisy label learning. Learning with Noisy Correspondence (NC) aims to address the mismatched pairs rather than typical annotation errors. Unlike traditional noisy label learning which primarily addresses incorrect annotations, NC shifts attention to incorrect correspondences between data samples, such as false positive and false negative correspondences.
Selected Publications
AI4Science
XLearning Group is dedicated to addressing core interdisciplinary challenges and developing tailored solutions. Our current research efforts focus on AI4LifeScience (Nat. Comm.'23, Nat. Comm.'25) and the exploration of Virtual Cell models to advance the understanding of biological systems. We warmly welcome collaborations and discussions with teams across diverse disciplines.
Clustering
Clustering is a classic and fundamental problem in machine learning, focused on partitioning instances into distinct clusters based on their inherent semantics in an unsupervised manner, which is closely intertwined with unsupervised representation learning, as both seek to uncover latent structures in data. Clustering serves as a cornerstone for various real-world applications, including anomaly detection, community discovery, and bioinformatics, etc.