The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem
when retrieving the person of interest across non-overlapping cameras. With the increasing …

In recent years, with the increasing demand for public safety and the rapid development of
intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot …

Video-based person re-identification aims to associate the video clips of the same person
across multiple non-overlapping cameras. Spatial-temporal representations can provide …

Video-based person re-identification (reID) aims to retrieve person videos with the same
identity as a query person across multiple cameras. Spatial and temporal distractors in …

Despite much recent progress in video-based person re-identification (re-ID), the current
state-of-the-art still suffers from common real-world challenges such as appearance …

Quantitative trading and investment decision making are intricate financial tasks that rely on
accurate stock selection. Despite advances in deep learning that have made significant …

In this paper, we present an efficient spatial-temporal representation for video person re-
identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for …

Due to the limited utilization of temporal relations in video re-id, the frame-level attention
regions of mainstream methods are partial and highly similar. To address this problem, we …

Video-based person re-identification aims to match pedestrians from video sequences
across non-overlapping camera views. The key factor for video person re-identification is to …