Person Search (PS) has become a major field because of its need in community and in the
field of research among researchers. This task aims to find a probe person from whole scene …

Person search detects and retrieves simultaneously a query person across uncropped
scene images captured by multiple non-overlapping cameras. In light of the deep learning …

Person re-identification is a challenging retrieval task that requires matching a person's
acquired image across non-overlapping camera views. In this paper we propose an effective …

Person search aims to simultaneously localize and identify a query person from realistic,
uncropped images, which can be regarded as the unified task of pedestrian detection and …

The goal of person search is to localize a target person from a gallery set of scene images,
which is extremely challenging due to large scale variations, pose/viewpoint changes, and …

Person re-identification has achieved great progress with deep convolutional neural
networks. However, most previous methods focus on learning individual appearance feature …

Weakly supervised semantic segmentation is a challenging task as it only takes image-level
information as supervision for training but produces pixel-level predictions for testing. To …

We propose a novel one-step transformer-based person search framework, PSTR, that
jointly performs person detection and re-identification (re-id) in a single architecture. PSTR …

In this work, we tackle the problem of person search, which is a challenging task consisted of
pedestrian detection and person re-identification~(re-ID). Instead of sharing representations …

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the
large-scale person reidentification task in videos. Different from the most existing methods …