no code implementations • 6 Dec 2022 • Hao Zeng, Wei zhang, Changjie Fan, Tangjie Lv, Suzhen Wang, Zhimeng Zhang, Bowen Ma, Lincheng Li, Yu Ding, Xin Yu
Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping.
no code implementations • 27 Oct 2022 • Rudong An, Wei zhang, Hao Zeng, Wei Chen, Zhigang Deng, Yu Ding
Then, AU feature maps and their corresponding AU masks are multiplied to generate AU masked features focusing on local facial region.
no code implementations • 23 Mar 2022 • Wei zhang, Feng Qiu, Suzhen Wang, Hao Zeng, Zhimeng Zhang, Rudong An, Bowen Ma, Yu Ding
Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features.
no code implementations • 8 Sep 2021 • Hao Zeng, Qiong Wu, Kunpeng Han, Junying He, Haoyuan Hu
In this paper, we investigate the online parcel assignment (OPA) problem, in which each stochastically generated parcel needs to be assigned to a candidate route for delivery to minimize the total cost subject to certain business constraints.
no code implementations • 9 Mar 2021 • Xin Qin, Hanbin Zhao, Guangchen Lin, Hao Zeng, Songcen Xu, Xi Li
In this paper, we propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization.
no code implementations • 20 Dec 2020 • Hao Zeng, Qingjie Liu, Mingming Zhang, Xiaoqing Han, Yunhong Wang
To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size.
no code implementations • 24 Jul 2020 • Hanbin Zhao, Hao Zeng, Xin Qin, Yongjian Fu, Hui Wang, Bourahla Omar, Xi Li
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network.
no code implementations • 9 Jun 2020 • Qingdong He, Zhengning Wang, Hao Zeng, Yijun Liu, Shuaicheng Liu, Bing Zeng
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.
no code implementations • 7 Jun 2020 • Qingdong He, Zhengning Wang, Hao Zeng, Yi Zeng, Yijun Liu
Accurate 3D object detection from point clouds has become a crucial component in autonomous driving.
Ranked #1 on
3D Object Detection
on KITTI Pedestrians Hard
no code implementations • 2 Dec 2019 • Hao Wang, Hao Zeng, Jiashan Wang
We propose a general framework of iteratively reweighted l1 methods for solving lp regularization problems.