1 code implementation • 19 Jul 2023 • Leilei Ma, Dengdi Sun, Lei Wang, Haifeng Zhao, Bin Luo
Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes.
Ranked #1 on Multi-Label Learning on COCO 2014
no code implementations • 17 Aug 2022 • Sachith Seneviratne, Jasper S. Wijnands, Kerry Nice, Haifeng Zhao, Branislava Godic, Suzanne Mavoa, Rajith Vidanaarachchi, Mark Stevenson, Leandro Garcia, Ruth F. Hunter, Jason Thompson
Analysis of overhead imagery using computer vision is a problem that has received considerable attention in academic literature.
no code implementations • 9 Jul 2022 • Deyin Liu, Lin Wu, Haifeng Zhao, Farid Boussaid, Mohammed Bennamoun, Xianghua Xie
Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN.
1 code implementation • 22 Mar 2022 • Songsong Wu, Hao Tang, Xiao-Yuan Jing, Haifeng Zhao, Jianjun Qian, Nicu Sebe, Yan Yan
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points.
no code implementations • 29 Oct 2020 • Jasper S. Wijnands, Haifeng Zhao, Kerry A. Nice, Jason Thompson, Katherine Scully, Jingqiu Guo, Mark Stevenson
The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma.
1 code implementation • 8 Oct 2019 • Kerry A. Nice, Jasper S. Wijnands, Ariane Middel, Jingcheng Wang, Yiming Qiu, Nan Zhao, Jason Thompson, Gideon D. P. A. Aschwanden, Haifeng Zhao, Mark Stevenson
To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types.
no code implementations • 14 May 2019 • Jasper S. Wijnands, Kerry A. Nice, Jason Thompson, Haifeng Zhao, Mark Stevenson
Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e. g., a photo) with the style of another (e. g., a painting).