Video person re-identification (V-ReID) can leverage rich spatial-temporal information
embedded in sequence data to achieve better accuracy. However, it is vulnerable to the …

In this paper, we propose a novel feature learning framework for video person re-
identification (re-ID). The proposed framework largely aims to exploit the adequate temporal …

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 …

Temporal cues embedded in videos provide important clues for person Re-Identification
(ReID). To efficiently exploit temporal cues with a compact neural network, this work …

In this work we propose to boost video-based person re-identification (Re-ID) by using
attribute-enhanced feature presentation. To this end, we not only try to use the ID-relevant …

This paper proposes the Global-Local Temporal Representation (GLTR) to exploit the multi-
scale temporal cues in video sequences for video person Re-Identification (ReID). GLTR is …

In recent years, the Transformer architecture has shown its superiority in the video-based
person re-identification task. Inspired by video representation learning, these methods …

In video person re-identification (Re-ID), the network must consistently extract features of the
target person from successive frames. Existing methods tend to focus only on how to use …

Pedestrians in videos are usually in a moving state, resulting in serious spatial misalignment
like scale variations and pose changes, which makes the video-based person re …

Video person re-identification (re-ID) methods extract richer features from video tracklets
than image-based ones and have received growing attention. However, existing supervised …