Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …

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 …

Thanks to the recent research development in contrastive learning, the gap of visual
representation learning between supervised and unsupervised approaches has been …

Attention mechanisms are now widely used in image captioning models. However, most
attention models only focus on visual features. When generating syntax related words, little …

Different from general face recognition, age-invariant face recognition (AIFR) aims at
matching faces with a big age gap. Previous discriminative methods usually focus on …

Abstract Neural Architecture Search (NAS) has been increasingly appealing to the society of
object Re-Identification (ReID), for that task-specific architectures significantly improve the …

Unsupervised visible-infrared person re-identification is a challenging task due to the large
modality gap and the unavailability of cross-modality correspondences. Cross-modality …

Unsupervised person re-identification (ReID) aims at learning discriminative identity features
without annotations. Recently, self-supervised contrastive learning has gained increasing …

Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by
measuring the feature similarity without considering the distribution discrepancy among …