no code implementations • ICCV 2021 • Yan Huang, Qiang Wu, Jingsong Xu, Yi Zhong, Zhaoxiang Zhang
This work argues that these approaches in fact are not aware of clothing status (i. e., change or no-change) of a pedestrian.
no code implementations • 13 May 2020 • Lu Zhang, Jian Zhang, Zhibin Li, Jingsong Xu
Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks.
no code implementations • ICCV 2019 • Yan Huang, Qiang Wu, JingSong Xu, Yi Zhong
We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance.
no code implementations • 4 Aug 2019 • Huaxi Huang, Jun-Jie Zhang, Jian Zhang, Jingsong Xu, Qiang Wu
A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric.
1 code implementation • 7 Apr 2019 • Huaxi Huang, Jun-Jie Zhang, Jian Zhang, Qiang Wu, Jingsong Xu
Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric.
no code implementations • 22 Nov 2016 • Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Jingsong Xu, Zhenmin Tang
To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.