1 code implementation • 10 Oct 2023 • Fei Wang, Kongzhang Tang, Hefeng Wu, Baoquan Zhao, Hao Cai, Teng Zhou
Compared with natural images, freehand sketches are much more flexible to depict various shapes, providing a high potential and valuable way for 3D human reconstruction.
no code implementations • 15 Mar 2022 • Mingjie Wang, Jun Zhou, Hao Cai, Minglun Gong
Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire.
1 code implementation • 9 Jun 2021 • Jingyuan Chen, Guanchen Ding, Yuchen Yang, Wenwei Han, Kangmin Xu, Tianyi Gao, Zhe Zhang, Wanping Ouyang, Hao Cai, Zhenzhong Chen
For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies.
no code implementations • 18 Dec 2020 • Mingjie Wang, Hao Cai, XianFeng Han, Jun Zhou, Minglun Gong
To battle the ingrained issue of accuracy degradation, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting.
no code implementations • 25 May 2020 • Mingjie Wang, Hao Cai, Jun Zhou, Minglun Gong
Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images.
2 code implementations • 22 Mar 2018 • Zili Yi, Zhiqin Chen, Hao Cai, Wendong Mao, Minglun Gong, Hao Zhang
The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features.