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AIML medical imaging technology achieves 98% in FDA trials
A ten-week pivotal clinical trial at TriCore Reference Laboratories in New Mexico during July and August 2015 tested APAS against a panel of microbiologists. C
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Number two in ImageNet Scene Parsing Challenge 2016
We’ve had another great year in the ImageNet competition.
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AIML joins the Australian Driverless Vehicle Initiative
The AIML (formally ACVT) has joined the Australian Driverless Vehicle Initiative (ADVI), the peak body for driverless vehicles in Australia.
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A new Machine Learning result in Quantum Physics
John Bastian and Anton van den Hengel are among the authors of a new paper just published in Nature Scientific Reports.
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We're 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.
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.
We beat Google at ImageNet Detection
The ImageNet Object Detection results are out, and we did extremely well!
Great Imagenet detection results
Last week was the deadline for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2015) large-scale object detection task. This is the primary challenge for image-based object detection. The challenge requires that you detect 200 classes of objects in a set of test images.
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’s best performance in predicting protein-protein interactions.
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In deep learning end-to-end training of segmentation is best
A research team (Dr. Guosheng Lin, Prof. Chunhua Shen, Prof. Ian Reid, Prof. Anton van den Hengel) at the School of Computer Science, The University of Adelaide developed innovative “Deep Structured Learning” techniques that set up the new state-of-the-art semantic image segmentation record in the PASCAL VOC Challenge, which is organised by the University of Oxford. The Adelaide team is the top one currently, outperforming teams from Microsoft Research, Oxford, University of California, Los Angles etc.
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