We’ve had another great year in the ImageNet competition.
John Bastian and Anton van den Hengel are among the authors of a new paper just published in Nature Scientific Reports.
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.
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.
The ImageNet Object Detection results are out, and we did extremely well!
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.
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.
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.
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.