Person re-identification: A retrospective on domain specific open challenges and future trends
Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …
which aims to automatically recognize and locate individuals across a multi-camera network …
Ice: Inter-instance contrastive encoding for unsupervised person re-identification
Unsupervised person re-identification (ReID) aims at learning discriminative identity features
without annotations. Recently, self-supervised contrastive learning has gained increasing …
without annotations. Recently, self-supervised contrastive learning has gained increasing …
Idm: An intermediate domain module for domain adaptive person re-id
Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …
Online pseudo label generation by hierarchical cluster dynamics for adaptive person re-identification
Adaptive person re-identification (adaptive ReID) targets at transferring learned knowledge
from the labeled source domain to the unlabeled target domain. Pseudo-label-based …
from the labeled source domain to the unlabeled target domain. Pseudo-label-based …
Generalizable person re-identification with relevance-aware mixture of experts
Abstract Domain generalizable (DG) person re-identification (ReID) is a challenging
problem because we cannot access any unseen target domain data during training. Almost …
problem because we cannot access any unseen target domain data during training. Almost …
Person re-identification via attention pyramid
In this paper, we propose an attention pyramid method for person re-identification. Unlike
conventional attention-based methods which only learn a global attention map, our attention …
conventional attention-based methods which only learn a global attention map, our attention …
Unrealperson: An adaptive pipeline towards costless person re-identification
The main difficulty of person re-identification (ReID) lies in collecting annotated data and
transferring the model across different domains. This paper presents UnrealPerson, a novel …
transferring the model across different domains. This paper presents UnrealPerson, a novel …
Meta pairwise relationship distillation for unsupervised person re-identification
Unsupervised person re-identification (Re-ID) remains challenging due to the lack of ground-
truth labels. Existing methods often rely on estimated pseudo labels via iterative clustering …
truth labels. Existing methods often rely on estimated pseudo labels via iterative clustering …
Reliability modeling and contrastive learning for unsupervised person re-identification
Unsupervised person re-identification (ReID) aims to learn discriminative identity features in
scenarios without a ground-truth. Fully unsupervised person ReID methods usually iterate …
scenarios without a ground-truth. Fully unsupervised person ReID methods usually iterate …
Joint clustering and discriminative feature alignment for unsupervised domain adaptation
Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target
domain by leveraging knowledge from a labeled source domain with a different but related …
domain by leveraging knowledge from a labeled source domain with a different but related …