Text based Person Retrieval

The CUHK-PEDES dataset is a caption-annotated pedestrian dataset. It contains 40,206 images over 13,003 persons. Images are collected from five existing person re-identification datasets, CUHK03, Market-1501, SSM, VIPER, and CUHK01 while each image is annotated with 2 text descriptions by crowd-sourcing workers. Sentences incorporate rich details about person appearances, actions, poses. Source: MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination Image Source: https://www.researchgate.net/figure/Image-samples-in-three-datasets-For-MSCOCO-and-Flickr30k-dataset-we-view-every-image_fig2_321095980

RSTPReid contains 20505 images of 4,101 persons from 15 cameras. Each person has 5 corresponding images taken by different cameras with complex both indoor and outdoor scene transformations and backgrounds in various periods of time, which makes RSTPReid much more challenging and more adaptable to real scenarios. Each image is annotated with 2 textual descriptions. For data division, 3701 (index < 18505), 200 (18505 <= index < 19505) and 200 (index >= 19505) identities are utilized for training, validation and testing, respectively (Marked by item 'split' in the JSON file). Each sentence is no shorter than 23 words.

One large-scale database for Text-to-Image Person Re-identification, i.e., Text-based Person Retrieval. Compared with existing databases, ICFG-PEDES has three key advantages. First, its textual descriptions are identity-centric and fine-grained. Second, the images included in ICFG-PEDES are more challenging, containing more appearance variability due to the presence of complex backgrounds and variable illumination. Third, the scale of ICFG-PEDES is larger.