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Category: Research

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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. For each image, algorithms must produce a set of annotations (ci,si,bi)of […]

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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 […]

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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. This result is based on the data set labelled ‘Physical Interaction Task in […]

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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. There are 18 teams all together and our attributes based image captioning framework currently achieves the best result on 3 evaluation metrics (BLEU-1,2,3) out of 7. We also achieve the top-5 ranking on the […]

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ACVT semantic image segmentation technique tops in the PASCAL VOC Challenge

Researchers at 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. Deep Learning is the […]

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CVPR 2015 Best Workshop Paper Award

In conjunction with the success of the Australian Centre for Robotic Vision’s workshops at CVPR2015 in Boston: Workshop on Semantics for Visual Reconstruction, Localization and Mapping Workshop on Visual Place Recognition in Changing Environments An ACRV team was also successful in attaining the Best Workshop Paper Award for the IEEE – Workshop on Computer Vision […]

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Honourable paper at CVPR’15

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. CVPR is the premier annual conference for Computer Vision research, and this recognition is a fantastic achievement for the team! Only 3 Best Paper Honourable Mention awards were […]

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Twelve papers at CVPR

The ACVT had 12 papers accepted to IEEE Computer Vision and Pattern Recognition 2015 (CVPR’15), one of the top 2 conferences in Computer Vision, which must be an Australian record. In terms of h5-index, CVPR ranks as the 7th venue in all Engineering and Computer Science after Nano and Nature journals. To put this in […]

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Best Paper Award for Prof Ian Reid at 3DV2014

Professor Ian Reid and colleagues from Oxford have been awarded the Best Paper Award at the International Conference on 3D Vision, held in Tokyo, Japan from 8-11 December 2014 The paper, titled “3D Tracking of Multiple Objects with Identical Appearance using RGB-D Input”, was authored by Carl Yuheng Ren, Victor Prisacariu, Olaf Kaehler, Ian Reid […]

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LBT Innovations presents APAS study results

LBT Innovations (ASX:LBT) has published the details of its extensive study into the accuracy of its Automated Plate Assessment System (APAS). Results from the study were released in a poster presentation at the Australian Society for Microbiology annual meeting in Melbourne this week. The poster confirms the headline result that, comparing the screening of hundreds of clinical samples by APAS […]

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The Australian Centre for Visual Technologies
Address

Level 5, Ingkarni Wardli,
The University of Adelaide,
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Adelaide, 5005

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