126 citations
- Tsinghua UniversityCN6 papers
- Peking UniversityCN4 papers
- Chinese Academy of SciencesCN3 papers
- City University of Hong KongHK3 papers
- Renmin University of ChinaCN3 papers
- Tencent (China)CN3 papers
- University of Science and Technology of ChinaCN3 papers
- Beihang UniversityCN2 papers
- Cardiff UniversityGB2 papers
- Hong Kong University of Science and TechnologyHK2 papers
- Kwai Chung HospitalCN2 papers
- National University of SingaporeSG2 papers
9 papers · 1 filter
DeViT: Deformed Vision Transformers in Video Inpainting
Jiayin Cai, Changlin Li, Xin Tao +2
This paper proposes a novel video inpainting method. We make three main contributions: First, we extended previous Transformers with patch alignment by introducing Deformed Patch-b…
Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels
Zipeng Ye, Mengfei Xia, Ran Yi +5
In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks. Using a fully convolutional network with the proposed DCKs, high-quality ta…
Semantic Image Matting
Yanan Sun, Chi-Keung Tang, Yu-Wing Tai
Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), an…
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion
Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained…
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Jiaming Li, Hongtao Xie, Jiahong Li +2
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound a…
Deformable Gabor Feature Networks for Biomedical Image Classification
Xuan Gong, Xin Xia, Wentao Zhu +3
In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the…