=== Important dates ===
Method Submission Deadline: May 31, 2025
Contest Paper Deadline: June 15, 2025
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=== Contest ===
Following the success of the previous edition presented during CAIP 2023, the Pedestrian Attribute Recognition (PAR) 2025 Contest is an international competition aimed at assessing methods for recognizing pedestrian attributes from images. We provide the participants with the Mivia PAR KD Dataset 2025, featuring newly annotated images with labels such as clothing color, gender and the presence or absence of a bag or hat. After the contest, the dataset—expanded with additional samples and annotations contributed by participants—will be made publicly available to the scientific community, with the goal to build one of the largest datasets for PAR with the considered set of annotations. Competing methods will be evaluated based on accuracy using a distinct private test set, separate from the training data. Recently, a wide variety of methods have been proposed to tackle the challenge of PAR in both effective and efficient ways. In the 2023 edition, the winning method, which leveraged Visual Question Answering (VQA), achieved remarkable success by integrating Large Language Models. This approach reached an impressive 92% accuracy on the contest’s private test set, highlighting the immense potential of Vision-Language Models (VLMs) in addressing complex PAR challenges. Considering the rapid advancements in VLMs over the past two years, we expect many of the proposed methods to take advantage of these cutting-edge technologies. However, the competition is not limited to a specific approach and every innovative solution is not only welcomed but highly valued, contributing to the ongoing progression of this field.
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=== Rules ===
The deadline for method submission is May 31, 2025. Submissions must be made via email, in which participants must share (either directly or via external links) the trained model, the code and a technical report of the method. The participants can obtain the training set, validation set and their annotations by sending an email, specifying their team name. They are allowed to use these provided training and validation samples and annotations but they may incorporate additional samples. However, the additional samples and annotations used must be made publicly available. Each participant must train a neural network to predict all the required pedestrian attributes for each sample. Teams are free to design novel neural network architectures, define new training procedures or propose innovative loss functions. Participants are highly encouraged to submit their contest papers via email by the deadline of June 15, 2025. The top three papers will be featured in the proceedings of the CAIP 2025 main conference. When submitting a paper, participants are requested to cite the official contest paper, which can be downloaded from the bibtex file or as follows:

Greco A., Vento B., “PAR Contest 2025: Pedestrian Attributes Recognition with Advanced Neural Networks”, 21st International Conference Computer Analysis of Images and Patterns, CAIP 2025

The detailed instructions can be downloaded here: https://mivia.unisa.it/par2025/
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The organizers,
Antonio Greco, University of Salerno, Italy
Bruno Vento, University of Naples – Federico II, Italy