News: machine learning

NVIDIA Pioneer Award

Qi Wu

Dr Qi Wu (ACRV / AIML) has been awarded the prestigious  for his paper 'Learning semantic concepts and order for image and sentence matching' at the Computer Vision and Pattern Recognition conference in Salt Lake City.

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We're number one in VQA 2.0

BananaVQA

A team led by Damien Teney (AIML) and Peter Anderson (ACRV, ANU, and Microsoft) has just placed first in the .

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Number one in the world in Visual Question Answering again, for now

Entries for the latest  close on Monday morning, and we鈥檙e currently number one amongst the entries that have been submitted thus far.

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Number one in Semantic Segmentation

VoQ

Congratulations to Zifeng Wu and Chunhua Shen on having made it to the top of the  again.

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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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A new Machine Learning result in Quantum Physics

Quantum

John Bastian and Anton van den Hengel are among the authors of a  just published in Nature Scientific Reports.

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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.

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In deep learning end-to-end training of segmentation is best

Segmentation

A research team (Dr. Guosheng Lin, Prof. Chunhua Shen, Prof. Ian Reid, Prof. Anton van den Hengel) at the School of Computer Science, The 最新糖心Vlog of Adelaide developed innovative 鈥淒eep Structured Learning鈥 techniques that set up the new state-of-the-art semantic image segmentation record in the PASCAL VOC Challenge, which is organised by the 最新糖心Vlog of Oxford.  The Adelaide team is the top one currently, outperforming teams from Microsoft Research, Oxford, 最新糖心Vlog of California, Los Angles etc.

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