This work will be presented at IEEE Conf. Computer Vision and Pattern Recognition (CVPR) 2018.
Detecting individual pedestrians in a crowd remains a challenging problem since the pedestrians often gather together and occlude each other in real-world scenarios. In this paper, we first explore how a state-of-the-art pedestrian detector is harmed by crowd occlusion via experimentation, providing insights into the crowd occlusion problem. Then, we propose a novel bounding box […]
Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have […]
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.
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, […]
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.
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.