The field of person re-identification is moving towards leveraging event cameras and aerial-ground platforms to improve performance and address challenges such as privacy protection and occlusions. Researchers are also exploring the use of attribute-guided contrastive learning frameworks and style-robust feature learning to enhance feature learning and bridge domain gaps. Additionally, there is a growing interest in analyzing contrails and their impact on climate, with new datasets and deep learning frameworks being developed to support improved contrail monitoring and calibration of physical models. Notable papers include:

• A paper introducing a large-scale RGB-event based person ReID dataset and a pedestrian attribute-guided contrastive learning framework, which effectively explores visual features from both RGB frames and event streams.
• A paper proposing a novel three-stream architecture for aerial-ground cross-modality video-based person Re-ID, which achieves significant performance gains across multiple evaluation protocols.
• A paper presenting a new open dataset of contrails recorded with a ground-based all-sky camera, which allows for detailed analysis of contrail lifecycle and supports improved contrail monitoring.