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

Human-centric visual tasks have attracted increasing research attention due to their
widespread applications. In this paper, we aim to learn a general human representation from …

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 search simultaneously detects and retrieves a query person from uncropped scene
images. Existing methods are either two-step or end-to-end. The former employs two …

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 …

Current research evaluates person search, multi-object tracking and multi-person pose
estimation as separate tasks and on different datasets although these tasks are very akin to …

Deep learning-based object detection is a fundamental but challenging problem in computer
vision field, has attracted a lot of study in recent years. State-of-the-art object detection …

Person search is an extremely challenging task that seeks to identify individuals through
joint person detection and person re-identification from uncropped real scene images …

We address the task of person search, that is, localizing and re-identifying query persons
from a set of raw scene images. Recent approaches are typically built upon OIMNet, a …

Mainstream person search models aim to jointly optimize person detection and re-
identification (ReID) in a one-step manner. Despite notable progress, existing one-step …