The lifelong person re-identification (LRe-ID) model retrieves a person across multiple
cameras in continuous data streams and learns new coming datasets incrementally …

In recent years, there has been a growing interest in synthetic data for several computer
vision applications, such as automotive, detection and tracking, surveillance, medical image …

In the evolving landscape of surveillance and security applications, the task of person re-
identification (re-ID) has significant importance, but also presents notable difficulties. This …

For robust person re-identification (Re-ID), the key is effectively learning the features of body
parts and their long-distance dependence. ResNet and Transformer are respectively good at …

Nuclei instance segmentation and classification in histology plays a major role in routine
pathology image examination, which enable morphological features analysis that further …

Person re-identification (re-id) is an autonomous process that uses raw surveillance images
to identify a person across multiple non-overlapping camera views without requiring any …

Person re-identification (Re-ID) has demonstrated remarkable performance when trained on
accurately annotated data. However, in practical applications, the presence of annotation …

Person re-identification (ReID) typically encounters varying degrees of occlusion in real-
world scenarios. While previous methods have addressed this using handcrafted partitions …

Person re-identification identifies pedestrians by analyzing image information from
surveillance videos. However, it faces challenges like occlusion, changing lighting, and …

At present, occlusion and similar appearance pose serious challenges to the task of person
re-identification. In this work, we propose an efficient multi-scale channel attention network …