Pedestrian Attribute Recognition and Attributed-based Person Retrieval Challenge
We cordially invite you to participate in our WACV'23 Pedestrian
Attribute Recognition and Attributed-based Person Retrieval Challenge
Challenge description: The challenge will use an extension of the UPAR
Dataset, which consists of images of pedestrians annotated for 40
binary attributes. For deployment and long-term use of
machine-learning algorithms in a surveillance context, the algorithms
must be robust to domain gaps that occur when the environment
changes. This challenge aims to spotlight the problem of domain gaps
in a real-world surveillance context and highlight the challenges and
limitations of existing methods to provide a direction for future
research. It will be divided in two competition tracks:
Track 1: Pedestrian Attribute Recognition: The task is to train an
attribute classifier that accurately predicts persons' semantic
attributes, such as age or clothing information, under domain
shifts.
Track 2: Attribute-based Person Retrieval: Attribute-based person
retrieval aims to find persons in a huge database of images called
gallery that match a specific attribute description. The goal of
this track is to develop an approach that takes binary attribute
queries and gallery images as input and ranks the images according
to their similarity to the query.
Challenge webpage: https://chalearnlap.cvc.uab.cat/challenge/52/description/
Tentative Schedule:
Start of the Challenge (development phase): Sep 19, 2022
Start of test phase: Oct 17, 2022
End of the Challenge: Oct 31, 2022
Release of final results: Nov 10, 2022