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We beat Google at ImageNet Detection

imagenet

The ImageNet Object Detection results are out, and we did extremely well!

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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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State of the art protein-protein interaction prediction

Sp

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

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 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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Second Scenes from Video workshop to be held in Chile

SFV

Michael Black and Anton van den Hengel are organising the Second Scenes from Video workshop, which will be held in the Colchagua Valley, one of Chile’s pre-eminent wine regions, and will include a variety of  functions and excursions exploring the region and its fare.

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Professor Wojciech Chojnacki granted Polish State Professorship

Prof. Wojciech Chojnacki was recently awarded the permanent state title of Professor conferred by the President of the Republic of Poland.

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World's best Coco

The Microsoft COCO Captioning Challenge is designed to spur the development of algorithms producing image captions that are informative and accurate.

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AIML semantic image segmentation technique tops the PASCAL VOC Challenge

Researchers at AIML (formerly ACVT) have developed new “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 Oxford University. Semantic image segmentation is one of the tasks and probably the most challenging one, which is to label each pixel in images.

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CVPR 2015 Best Workshop Paper Award

CVPR 2015

In conjunction with the success of the Australian Centre for Robotic Vision’s workshops at CVPR2015 in Boston:

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Honourable paper at CVPR 2015

Congratulations to TJ, Pulak, Anders and David. Their paper, Efficient Globally Optimal Consensus Maximisation with Tree Search, recently was awarded a Best Paper Honourable Mention at CVPR.

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