Pedestrian Attribute Recognition (PAR) Contest 2023 - CAIP 2023 	 === Call for submissions ===
Pedestrian Attribute Recognition (PAR) Contest 2023 International Conference on Computer Analysis of Images and Patterns CAIP 2023 Website: https://mivia.unisa.it/par2023/ or https://par2023.unisa.it
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=== Important dates ===
Submission Deadline: June 30th, 2023
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=== Contest ===
We are pleased to announce that Pedestrian Attribute Recognition (PAR) Contest 2023 will be held by the 20th International Conference on Computer Analysis of Images and Patterns CAIP 2023. The Pedestrian Attribute Recognition (PAR) Contest is a competition among methods for pedestrian attributes recognition from images. For the contest, we propose the use of a novel training set, the MIVIA PAR Dataset, partially annotated with five pedestrian attributes, namely color of the clothes (top and bottom), gender (female, male), bag (y/n), hat (y/n), and we restrict the competition to methods based on multi-task learning. The participants are encouraged to use additional samples or to produce themselves the missing annotations; this possibility is allowed in the competition only under the constraint that the additional samples and annotations are made publicly available, to give a relevant contribution to the diffusion of public datasets for pedestrian attributes recognition. After the contest, the dataset, also augmented with additional samples and annotations produced by the participants, will be made publicly available for the scientific community and will hopefully become among the biggest dataset of pedestrian attributes with this set of annotations. The performance of the competing methods will be evaluated in terms of accuracy on a private test set composed by images that are different from the ones available in the training set.
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=== Rules ===
The deadline for the submission of the methods is 30th June, 2023. The submission must be done with an email in which the participants share (directly or with external links) the trained model, the code and the report. The participants can receive the training set, the validation set and their annotations by sending an email to par2023@unisa.it, in which they also communicate the name of the team.  The participants can use these training and validation samples and annotations, but they can also use additional samples and/or add the missing labels, under the constraint that they make the additional samples and annotations publicly available. The participants must provide, for each sample, the prediction for all the considered pedestrian attributes, by training their multi-task neural network. For this reason, the validation set contains only fully annotated pedestrian samples.  The teams are free to design novel neural network architectures or to define novel training procedures and loss functions for multi-task learning. Particularly welcome are the methods dealing with the missing labels.  The participants must submit their trained model and their code by carefully following the detailed instructions reported in the website.  The participants are strongly encouraged to submit a contest paper to CAIP 2023, whose deadline is 10th July, 2023. The contest paper must be also sent by email to the organizers. Otherwise, the participants must produce a brief PDF report of the proposed method. The detailed instructions of the proposed method can be downloaded here: https://mivia.unisa.it/par2023/ (or https://par2023.unisa.it).
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The organizers, Antonio Greco Bruno Vento