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Category: deep learning

Video demo of our semantic segmentation on the Cityscape dataset

This work was presented in IEEE Conf Computer Vision Pattern Recognition 2017 in Hawaii

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No. 1 on Cityscapes semantic image segmentation challenge

Adelaide team is No. 1 on Cityscapes semantic segmentation. See our paper. The Cityscapes dataset is a large-scale dataset for semantic urban scene understanding, containing a diverse set of street scene video recordings from 50 cities. It’s one of the important benchmark datasets for autonomous driving, developed by Daimler AG.

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State-of-the-art image processing techniques developed here in Adelaide

The Adelaide Machine Learning Group has developed a unified image processing technique based on Deep Learning, which can be applied to various image enhancement tasks such as, not limited to, Image Denoising, Image Inpainting, Image Super-resolution, Image Deblur. This work will be presented at The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Spain, […]

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No. 2 on the ImageNet Challenge 2016 for the task of Scene Parsing

Adelaide team attended ImageNet Challenge 2016 on the task of Scene Parsing and won the 2nd place. The ImageNet Scene Parsing competition requires entrants to correctly label each pixel into 150 categories (building, road, car, computer, toy shop, cat, horse, and so on) for photographs from Flickr and various search engines with as few errors […]

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No. 1 on ICDAR 2015 Challenge 2 “Focused Scene Text” for the task of End-to-End Scene Text Recognition

As of March 2016, we achieved No. 1 on ICDAR 2015 Challenge 2 “Focused Scene Text” for the task of End-to-End Scene Text Recognition. See the Leaderboard. Details can be found in the technical report.

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No. 1 on Pascal VOC semantic image segmentation challenge

We design new methods for semantic pixel labelling, which set the new record. Details can be found in our technical report. See the PASCAL VOC Leaderboard.

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