[et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][dsm_text_divider header=”Contests” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”][\/dsm_text_divider][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.2″ _module_preset=”default” background_color=”rgba(48,135,193,0.19)” custom_margin=”0px||0px||true|false” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
PAR Contest 2023: Pedestrian Attribute Recognition with Multi-Task Neural Networks<\/strong><\/strong><\/p>\n 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, annotated with five pedestrian attributes, namely color of the clothes (top and bottom), gender (female, male), bag (presence or absence), hat (presence or absence), and we restrict the competition to methods based on multi-task learning. Since not all the training samples are annotated with all the labels, the participants may also propose a learning procedure designed for dealing with missing labels.<\/p>\n 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.<\/p>\n 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.<\/p>\n More info<\/a><\/strong><\/p>\n Website<\/strong>: http:\/\/par2023.unisa.it<\/a> & https:\/\/mivia.unisa.it\/par2023\/<\/a><\/p>\n Submission will be done through www.easyacademia.org\/caip2023.<\/a> Click here<\/a> for more information.\u00a0<\/p>\n Method Submission Deadline:<\/strong> 30th June 2023<\/p>\n Paper Submission Deadline:<\/strong> 10th July 2023<\/p>\n <\/p>\n Antonio Greco<\/em>, University of Salerno, Italy<\/p>\n Bruno Vento<\/em>, University of Napoli, Italy<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Contests<\/span><\/h3>\n\t\t\t\t