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Contrastive Multiple Instance Learning for Weakly Supervised Person ReID
The acquisition of large-scale, precisely labeled datasets for person re-identification (ReID) poses a significant challenge. Weakly supervised ReID has begun to address this... -
MARS: A video benchmark for large-scale person re-identification
MARS is an extension of the Market-1501 dataset [51]. It has been collected from six near-synchronized cameras. It consists of 1,261 different pedestrians, who are captured by... -
SYSU-MM01 dataset
The SYSU-MM01 dataset is a popular dataset for visible-infrared person re-identification. It contains 491 identities with 4 VIS images and 2 IR images captured by 4 cameras. -
RegDB dataset
The RegDB dataset is a popular dataset for visible-infrared person re-identification. It contains 412 identities with 10 VIS images and 10 IR images captured by a pair of... -
Low-Light Cross-Modality (LLCM) dataset
The LLCM dataset is a challenging low-light cross-modality dataset for visible-infrared person re-identification. It contains 46,767 bounding boxes of 1,064 identities captured... -
RRD-Campus
Person Re-Identification (ReID) aims to recognize a person-of-interest across different places and times. RF-ReID uses radio frequency (RF) signals for longterm person ReID. -
Market1501
Re-identification (Re-ID) problems aim to determine corresponding targets across multiple cameras or different locations in the same camera from the gallery set with a given probe. -
Beyond part models: Person retrieval with refined part pooling (and a strong ...
The dataset is used for person re-identification and image retrieval. -
Unlabeled samples generated by GAN improve the person re-identification basel...
A dataset for unsupervised person re-identification using Generative Adversarial Networks (GANs). -
Scalable person re-identification: A benchmark
The Market-1501 dataset is a large-scale benchmark for person re-identification. -
ICFG-PEDES
Text-based Person Search (TPS) task is targeted on retrieving pedestrians to match text descriptions instead of query images.