challenge https://www.adelaide.edu.au/aiml/ en Podium finish in global AI race contest https://www.adelaide.edu.au/aiml/news/list/2022/07/12/podium-finish-in-global-ai-race-contest An AIML team has scored double pole positions in a global virtual motor racing event that saw hundreds of AI researchers and engineers compete to build high performance virtual race cars. July 14 2022 Eddie Major https://www.adelaide.edu.au/aiml/news/list/2022/07/12/podium-finish-in-global-ai-race-contest 2nd place in xVIEW Challenge https://www.adelaide.edu.au/aiml/news/list/2018/11/06/2nd-place-in-xview-challenge A team from AIML featuring Jamie Sherrah and Phil Roberts, and DST Group’s Victor Stamatescu has beaten more than 4000 submissions from around the world to gain second place in the Defense Innovation Unit’s (DIU) xVIEW Challenge. November 06 2018 Ian Will https://www.adelaide.edu.au/aiml/news/list/2018/11/06/2nd-place-in-xview-challenge First place! https://www.adelaide.edu.au/aiml/news/list/2018/08/23/first-place Results for the REFUGE Retinal Fundus Glaucoma Challenge are in! First place in the Segmentation leaderboard and also in the Segmentation of Nuclei competition. August 23 2018 Ian Will https://www.adelaide.edu.au/aiml/news/list/2018/08/23/first-place We're number one in VQA 2.0 https://www.adelaide.edu.au/aiml/news/list/2017/07/27/were-number-one-in-vqa-20 A team led by Damien Teney (AIML) and Peter Anderson (ACRV, ANU, and Microsoft) has just placed first in the VQA 2.0 challenge. July 27 2017 Ian Will https://www.adelaide.edu.au/aiml/news/list/2017/07/27/were-number-one-in-vqa-20 Number one in the world in Visual Question Answering again, for now https://www.adelaide.edu.au/aiml/news/list/2017/06/23/number-one-in-the-world-in-visual-question-answering-again-for-now Entries for the latest VQA v2 challenge close on Monday morning, and we’re currently number one amongst the entries that have been submitted thus far. June 23 2017 Ian Will https://www.adelaide.edu.au/aiml/news/list/2017/06/23/number-one-in-the-world-in-visual-question-answering-again-for-now Number one in Semantic Segmentation https://www.adelaide.edu.au/aiml/news/list/2017/02/02/number-one-in-semantic-segmentation Congratulations to Zifeng Wu and Chunhua Shen on having made it to the top of the Cityscapes leaderboard again. February 02 2017 Ian Will https://www.adelaide.edu.au/aiml/news/list/2017/02/02/number-one-in-semantic-segmentation Number two in ImageNet Scene Parsing Challenge 2016 https://www.adelaide.edu.au/aiml/news/list/2016/09/28/number-two-in-imagenet-scene-parsing-challenge-2016 We’ve had another great year in the ImageNet competition. September 28 2016 Ian Will https://www.adelaide.edu.au/aiml/news/list/2016/09/28/number-two-in-imagenet-scene-parsing-challenge-2016 We're in the top 5 groups the world https://www.adelaide.edu.au/aiml/news/list/2016/04/28/were-in-the-top-5-groups-the-world The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is double blind reviewed (on full papers), and has the best citation rate in the field of computer vision and pattern recognition, according to the h5-index, a citation measure for the recent five years. April 28 2016 Ian Will https://www.adelaide.edu.au/aiml/news/list/2016/04/28/were-in-the-top-5-groups-the-world 10 PAMIs and 28 CVPRs in just over a year https://www.adelaide.edu.au/aiml/news/list/2016/04/19/10-pamis-and-28-cvprs-in-just-over-a-year The AIML (formally ACVT) has had 10 journal articles published in IEEE Pattern Analysis and Machine Intelligence, and 28 papers in the IEEE Conference on Computer Vision and Pattern Recognition, in the 16 months since January 2015. April 19 2016 Ian Will https://www.adelaide.edu.au/aiml/news/list/2016/04/19/10-pamis-and-28-cvprs-in-just-over-a-year We beat Google at ImageNet Detection https://www.adelaide.edu.au/aiml/news/list/2015/12/11/we-beat-google-at-imagenet-detection The ImageNet Object Detection results are out, and we did extremely well! December 11 2015 Ian Will https://www.adelaide.edu.au/aiml/news/list/2015/12/11/we-beat-google-at-imagenet-detection