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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 …
Source-free domain adaptive human pose estimation
Abstract Human Pose Estimation (HPE) is widely used in various fields, including motion
analysis, healthcare, and virtual reality. However, the great expenses of labeled real-world …
analysis, healthcare, and virtual reality. However, the great expenses of labeled real-world …
Part-based pseudo label refinement for unsupervised person re-identification
Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …
Shape-erased feature learning for visible-infrared person re-identification
Due to the modality gap between visible and infrared images with high visual ambiguity,
learning diverse modality-shared semantic concepts for visible-infrared person re …
learning diverse modality-shared semantic concepts for visible-infrared person re …
Discover cross-modality nuances for visible-infrared person re-identification
Visible-infrared person re-identification (Re-ID) aims to match the pedestrian images of the
same identity from different modalities. Existing works mainly focus on alleviating the …
same identity from different modalities. Existing works mainly focus on alleviating the …
Self-paced contrastive learning with hybrid memory for domain adaptive object re-id
Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …
labeled source domain to the unlabeled target domain to tackle the open-class re …
Uncertainty modeling for out-of-distribution generalization
Though remarkable progress has been achieved in various vision tasks, deep neural
networks still suffer obvious performance degradation when tested in out-of-distribution …
networks still suffer obvious performance degradation when tested in out-of-distribution …
Cluster contrast for unsupervised person re-identification
Thanks to the recent research development in contrastive learning, the gap of visual
representation learning between supervised and unsupervised approaches has been …
representation learning between supervised and unsupervised approaches has been …
Unsupervised person re-identification via multi-label classification
The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …
features without true labels. This paper formulates unsupervised person ReID as a multi …