no code implementations • ECCV 2020 • Fang Liu, Changqing Zou, Xiaoming Deng, Ran Zuo, Yu-Kun Lai, Cuixia Ma, Yong-Jin Liu, Hongan Wang
Sketch-based image retrieval (SBIR) has been a popular research topic in recent years.
1 code implementation • 5 Mar 2024 • Miaomiao Li, Jiaqi Zhu, Yang Wang, Yi Yang, Yilin Li, Hongan Wang
Weakly supervised text classification (WSTC), also called zero-shot or dataless text classification, has attracted increasing attention due to its applicability in classifying a mass of texts within the dynamic and open Web environment, since it requires only a limited set of seed words (label names) for each category instead of labeled data.
no code implementations • ICCV 2023 • Baowen Zhang, Jiahe Li, Xiaoming Deng, yinda zhang, Cuixia Ma, Hongan Wang
In this paper, we propose a novel self-supervised approach to learn neural implicit shape representation for deformable objects, which can represent shapes with a template shape and dense correspondence in 3D.
no code implementations • ICCV 2023 • Wentian Qu, Zhaopeng Cui, yinda zhang, Chenyu Meng, Cuixia Ma, Xiaoming Deng, Hongan Wang
Hand-object interaction understanding and the barely addressed novel view synthesis are highly desired in the immersive communication, whereas it is challenging due to the high deformation of hand and heavy occlusions between hand and object.
no code implementations • 17 Nov 2022 • Xurong Xie, Xunying Liu, Hui Chen, Hongan Wang
Modeling the speaker variability is a key challenge for automatic speech recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 29 Mar 2022 • Jian Cheng, Yanguang Wan, Dexin Zuo, Cuixia Ma, Jian Gu, Ping Tan, Hongan Wang, Xiaoming Deng, yinda zhang
3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand.
Ranked #1 on Hand Pose Estimation on ICVL Hands
no code implementations • 28 Oct 2021 • Kevin Maher, Zeyuan Huang, Jiancheng Song, Xiaoming Deng, Yu-Kun Lai, Cuixia Ma, Hao Wang, Yong-Jin Liu, Hongan Wang
We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.
2 code implementations • 4 Jan 2021 • Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang
Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community.
no code implementations • ICCV 2021 • Baowen Zhang, Yangang Wang, Xiaoming Deng, yinda zhang, Ping Tan, Cuixia Ma, Hongan Wang
In this paper, we propose a novel deep learning framework to reconstruct 3D hand poses and shapes of two interacting hands from a single color image.
Ranked #6 on 3D Interacting Hand Pose Estimation on InterHand2.6M
1 code implementation • 6 Feb 2018 • Fengchun Qiao, Naiming Yao, Zirui Jiao, Zhihao LI, Hui Chen, Hongan Wang
Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions.
no code implementations • 7 Apr 2017 • Xiaoming Deng, Shuo Yang, yinda zhang, Ping Tan, Liang Chang, Hongan Wang
We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image.
no code implementations • 8 Dec 2016 • Xiaoming Deng, Ye Yuan, Yinda Zhang, Ping Tan, Liang Chang, Shuo Yang, Hongan Wang
Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.
no code implementations • CVPR 2015 • Hui Chen, Jiangdong Li, Fengjun Zhang, Yang Li, Hongan Wang
We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications.