News: Conference
AIML student鈥檚 research in top 2% at prestigious global computer vision conference
Congratulations to AIML PhD student Wei Yin who's work is in the top 2% of all papers accepted by CVPR!
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AIML confirmed as 2nd in the world for computer vision research
Researchers from AIML have 28 papers accepted for presentation at the virtual CVPR (Computer Vision and Pattern Recognition) conference coming up in June 2021. This further solidifies AIML's ranking of 2nd in the world for computer vision research published over the last 5 years.
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A surgeon, an engineer and a PhD student walk into a virtual classroom. This is Robotic Vision Summer School
Sixteen graduate students and industry professionals attended the South 最新糖心Vlogn node of the 2021 , held at the 最新糖心Vlogn Institute for Machine Learning (AIML) at the 最新糖心Vlog of Adelaide over January 31 to February 5.
MICCAI 2018
AIML research was well represented at the 2018 international conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain.
ICCVG 2018
Dr Zygmunt Szpak recently attended the International Conference on Computer Vision and Graphics 2018 in Warsaw, Poland. He was invited to deliver the plenary talk based on his research paper A Comprehensive Image Formation Model as a Tool for Parameter Estimation at the Limits of Resolution. Read it .
ECCV 2018
Double congratulations to our PhD student Zhipeng Cai who had two first-authored papers accepted as oral presentations at the in Munich, Germany (and presented them back to back!). Congrats also to his supervisor, Dr Tat-Jun Chin.
Google Summer of Code
We were involved in our very first . Dr Zygmunt Szpak mentored student Arijit Kar from the Indian Institute of Technology Kharagpur.
Another great CVPR result
The group had 11 CVPR papers accepted this year, which is another incredible result.
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.
State of the art protein-protein interaction prediction
In another indication that the Machine Learning behind most Computer Vision Problems has more general applicability, we have just had a paper accepted which shows that the approach we developed for pedestrian detection achieves the world鈥檚 best performance in predicting protein-protein interactions.
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