Machine learning is largely dependent on data and labels from multiple sources. Multi-source data can vary in views, modalities and distributions. Multi-source labels can have diverse label space and annotation biases. Our group aims at mining useful information from such multi-sourced visual data. The research can be applied in many areas, such as object detection, multi-object tracking, anomaly detection and smart monitoring.