Submission website: <\/strong> https:\/\/easychair.org\/conferences\/?conf=dogage19<\/a> Automatic age estimation is a challenging problem attracting attention of the computer vision and pattern recognition communities due to its many practical applications. Artificial neural networks, such as CNNs are a popular tool for tackling this problem, and several datasets which can be used for training models are available. Organisers:<\/strong> This challenge is organized in a collaboration between Tech4Animals Lab, University of Haifa, St. Petersburg Electrotechnical University “LETI”, and School of Environmental and Life Sciences, University of Salford. <\/p>\n\n\n\n Contact: annazam AT is.haifa.ac.il Submission website: <\/strong> https:\/\/easychair.org\/conferences\/?conf=dogage19<\/a> <\/p>\n","protected":false},"excerpt":{"rendered":" C1. The DogAge Challenge – Automatic Dog Age Estimation Submission website: https:\/\/easychair.org\/conferences\/?conf=dogage19 Deadline: September 10, 2019. Automatic age estimation is a challenging problem attracting attention of the computer vision and pattern recognition communities due to its many practical applications. Artificial neural networks, such as CNNs are a popular tool for tackling this problem, and several … Continue reading “Competitions”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":68,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/pages\/1657"}],"collection":[{"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/comments?post=1657"}],"version-history":[{"count":8,"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/pages\/1657\/revisions"}],"predecessor-version":[{"id":1811,"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/pages\/1657\/revisions\/1811"}],"up":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/pages\/68"}],"wp:attachment":[{"href":"https:\/\/e-nns.org\/icann2019\/wp-json\/wp\/v2\/media?parent=1657"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Deadline: September 10, 2019.<\/p>\n\n\n\n
Despite the fact that dogs are the most well studied species in animal science, and that ageing processes in dogs are in many aspects similar to those of humans, the problem of age estimation for dogs has so far been overlooked. For instance, age estimation can be a useful tool for assessing animal welfare.
The goal of this challenge is developing models that will accurately predict apparent dog age. To this end we make available the DogAge dataset which consists of a small set high quality data collected and verified by animal experts, and a larger set of images from downloaded from Petfinder website. Both sets are divided into three groups: young, adult and senior. <\/p>\n\n\n\n
More information and details about submission on the challenge website<\/a>.<\/p>\n\n\n\n
Deadline: September 10, 2019. <\/p>\n\n\n\n
\n\n\n\n