no code implementations • CVPR 2021 • Sijie Song, Xudong Lin, Jiaying Liu, Zongming Guo, Shih-Fu Chang
In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics.
no code implementations • 31 Jan 2020 • Sijie Song, Jiaying Liu, Yanghao Li, Zongming Guo
In this work, we propose a Modality Compensation Network (MCN) to explore the relationships of different modalities, and boost the representations for human action recognition.
1 code implementation • ECCV 2020 • Shuai Yang, Zhangyang Wang, Jiaying Liu, Zongming Guo
We present a sketch refinement strategy, as inspired by the coarse-to-fine drawing process of the artists, which we show can help our model well adapt to casual and varied sketches without the need for real sketch training data.
1 code implementation • 11 Sep 2019 • Jiaxiang Tang, Wei Hu, Xiang Gao, Zongming Guo
In particular, we cast the graph optimization problem as distance metric learning to capture pairwise similarities of features in each layer.
no code implementations • 22 Jul 2019 • Wei Hu, Xiang Gao, Gene Cheung, Zongming Guo
In this work, we assume instead the availability of a relevant feature vector $\mathbf{f}_i$ per node $i$, from which we compute an optimal feature graph via optimization of a feature metric.
1 code implementation • ICCV 2019 • Shuai Yang, Zhangyang Wang, Zhaowen Wang, Ning Xu, Jiaying Liu, Zongming Guo
In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter.
no code implementations • 23 Apr 2019 • Zeqing Fu, Wei Hu, Zongming Guo
With the development of 3D laser scanning techniques and depth sensors, 3D dynamic point clouds have attracted increasing attention as a representation of 3D objects in motion, enabling various applications such as 3D immersive tele-presence, gaming and navigation.
no code implementations • 23 Apr 2019 • Xiang Gao, Wei Hu, Zongming Guo
In this paper, we propose Graph Learning Neural Networks (GLNNs), which exploit the optimization of graphs (the adjacency matrix in particular) from both data and tasks.
no code implementations • 29 Dec 2018 • Junkun Qi, Wei Hu, Zongming Guo
With the development of 3D sensing technologies, point clouds have attracted increasing attention in a variety of applications for 3D object representation, such as autonomous driving, 3D immersive tele-presence and heritage reconstruction.
no code implementations • 16 Dec 2018 • Shuai Yang, Jiaying Liu, Wenjing Wang, Zongming Guo
The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images.
no code implementations • 29 Nov 2018 • Xiang Gao, Wei Hu, Jiaxiang Tang, Jiaying Liu, Zongming Guo
In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the spatio-temporal variation in the data.
Ranked #2 on Skeleton Based Action Recognition on Florence 3D
no code implementations • 28 Nov 2018 • Wei Hu, Gusi Te, Ju He, Dong Chen, Zongming Guo
Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks.
no code implementations • 9 Oct 2018 • Shuai Yang, Jiaying Liu, Wenhan Yang, Zongming Guo
The stylization is then followed by a context-aware layout design algorithm, where cues for both seamlessness and aesthetics are employed to determine the optimal layout of the shape in the background.
no code implementations • 28 Sep 2018 • Zeqing Fu, Wei Hu, Zongming Guo
Hence, leveraging on recent advances in graph signal processing, we propose an efficient point cloud inpainting method, exploiting both the local smoothness and the non-local self-similarity in point clouds.
1 code implementation • 8 Jun 2018 • Gusi Te, Wei Hu, Zongming Guo, Amin Zheng
Leveraging on spectral graph theory, we treat features of points in a point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial approximation.
no code implementations • CVPR 2018 • Jiaying Liu, Wenhan Yang, Shuai Yang, Zongming Guo
In this paper, we address the problem of video rain removal by constructing deep recurrent convolutional networks.
no code implementations • 20 Jan 2017 • Sifeng Xia, Wenhan Yang, Jiaying Liu, Zongming Guo
In particular, we infer the HF information based on both the LR image and similar HR references which are retrieved online.
1 code implementation • CVPR 2017 • Shuai Yang, Jiaying Liu, Zhouhui Lian, Zongming Guo
It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example.
2 code implementations • CVPR 2017 • Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, Shuicheng Yan
Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output.
no code implementations • 3 May 2016 • Mading Li, Jiaying Liu, Zhiwei Xiong, Xiaoyan Sun, Zongming Guo
In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models.
no code implementations • 29 Apr 2016 • Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo, Shuicheng Yan
To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details.