no code implementations • 2 Apr 2024 • Chaitali Bhattacharyya, Hanxiao Wang, Feng Zhang, Sungho Kim, Xiatian Zhu
To address this critical issue, we investigate the impact of enhancing training data diversity on representative detection methods.
no code implementations • 23 Apr 2023 • Ali Lazrak, Hanxiao Wang, Jiongmin Yong
We investigate a linear quadratic stochastic zero-sum game where two players lobby a political representative to invest in a wind turbine farm.
no code implementations • ICCV 2023 • Jingen Jiang, Mingyang Zhao, Shiqing Xin, Yanchao Yang, Hanxiao Wang, Xiaohong Jia, Dong-Ming Yan
We propose a novel and efficient method for reconstructing manifold surfaces from point clouds.
no code implementations • 18 Nov 2019 • Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable.
no code implementations • 25 Jan 2019 • Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training "models from scratch," and methods that adapt existing models, trained on the presented training environment, to the new scenario are required.
no code implementations • CVPR 2019 • Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
To bridge the gap, we propose a novel low-dimensional embedding of visual instances that is "visually semantic."
no code implementations • ICCV 2019 • Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff, Vitaly Ablavsky
We consider the problem of fine-grained classification on an edge camera device that has limited power.
no code implementations • 25 Jun 2018 • Hanxiao Wang, Xiatian Zhu, Shaogang Gong, Tao Xiang
Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time.
1 code implementation • 19 Mar 2018 • Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time.
no code implementations • 10 Jul 2017 • Xu Lan, Hanxiao Wang, Shaogang Gong, Xiatian Zhu
Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping.
no code implementations • 5 Dec 2016 • Hanxiao Wang, Shaogang Gong, Tao Xiang
Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available.
no code implementations • 5 Dec 2016 • Hanxiao Wang, Shaogang Gong, Xiatian Zhu, Tao Xiang
Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate.