The Task: The challenge will use an extension of the UPAR Dataset [1], 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. The Dataset: We will use an extension of the UPAR dataset [1]. The challenge dataset consists of the harmonization of three public datasets (PA100K [2], PETA [3], and Market1501-Attributes [4]) and a private test set. 40 binary attributes have been unified between those for which we provide additional annotations. This dataset enables the investigation of PAR methods' generalization ability under different attribute distributions, viewpoints, varying illumination, and low resolution. The Tracks: This challenge is split into two tracks associated with semantic pedestrian attributes, such as gender or clothing information: Pedestrian Attribute Recognition (PAR) and attribute-based person retrieval. Both tracks build on the same data sources but will have different evaluation criteria. There are three different dataset splits for both tracks that use different training domains. Each track evaluates how robust a given method is to domain shifts by training on limited data from a specific limited domain and evaluating using data from unseen domains. 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. The Phases: Each track will be composed of two phases, i.e., the development and test phases. During the development phase, public training data will be released, and participants must submit their predictions concerning a validation set. At the test (final) phase, participants will need to submit their results for the test data, which will be released just a few days before the end of the challenge. As we progress into the test phase, validation annotations will become available together with the test images for the final submission. At the end of the challenge, participants will be ranked using the public test data and additional data that is kept private. It is important to note that this competition involves submitting results and code. Therefore, participants will be required to share their code and trained models after the end of the challenge (with detailed instructions) so that the organizers can reproduce the results submitted at the test phase in a code verification stage. Verified code will be applied to a private test dataset for final ranking. The organizers will evaluate the top submissions on the public leaderboard on the private test set to determine the 3 top winners of the challenge. At the end of the challenge, top-ranked methods that pass the code verification stage will be considered valid submissions and compete for any prize that may be offered.