1 code implementation • ACL (InterNLP) 2021 • Michael Glass, Md Faisal Mahbub Chowdhury, Yu Deng, Ruchi Mahindru, Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Nandana Mihindukulasooriya
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach.
no code implementations • 20 Mar 2024 • Yu Deng, Duomin Wang, Baoyuan Wang
In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis.
no code implementations • 19 Mar 2024 • Yishu Wei, Yu Deng, Cong Sun, Mingquan Lin, Hongmei Jiang, Yifan Peng
This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation.
1 code implementation • 23 Jan 2024 • Yu Zhang, Yunyi Zhang, Yanzhen Shen, Yu Deng, Lucian Popa, Larisa Shwartz, ChengXiang Zhai, Jiawei Han
In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i. e., those without seed entities).
no code implementations • 30 Nov 2023 • Yu Deng, Duomin Wang, Xiaohang Ren, Xingyu Chen, Baoyuan Wang
The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning, to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data.
no code implementations • 29 Nov 2023 • Duomin Wang, Bin Dai, Yu Deng, Baoyuan Wang
In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives.
no code implementations • 29 Sep 2023 • Linghao Yang, Yanmin Wu, Yu Deng, Rui Tian, Xinggang Hu, Tiefeng Ma
Subsequently, in the part of object state estimation, we propose a tightly coupled optimization model for object pose and scale estimation, incorporating hybrids constraints into a novel dual sliding window optimization framework for joint estimation.
no code implementations • ICCV 2023 • Xingyu Chen, Yu Deng, Baoyuan Wang
Improving the photorealism via CNN-based 2D super-resolution can break the strict 3D consistency, while keeping the 3D consistency by learning high-resolution 3D representations for direct rendering often compromises image quality.
no code implementations • 5 Dec 2022 • Yu Zhang, Yunyi Zhang, Yucheng Jiang, Martin Michalski, Yu Deng, Lucian Popa, ChengXiang Zhai, Jiawei Han
Given a few seed entities of a certain type (e. g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds.
1 code implementation • CVPR 2023 • Duomin Wang, Yu Deng, Zixin Yin, Heung-Yeung Shum, Baoyuan Wang
We present a novel one-shot talking head synthesis method that achieves disentangled and fine-grained control over lip motion, eye gaze&blink, head pose, and emotional expression.
no code implementations • CVPR 2023 • Yu Deng, Baoyuan Wang, Heung-Yeung Shum
We introduce a novel detail manifolds reconstructor to learn 3D-consistent fine details on the radiance manifolds from monocular images, and combine them with the coarse radiance manifolds for high-fidelity reconstruction.
1 code implementation • 12 Oct 2022 • Yue Wu, Yu Deng, Jiaolong Yang, Fangyun Wei, Qifeng Chen, Xin Tong
To achieve meaningful control over facial expressions via deformation, we propose a 3D-level imitative learning scheme between the generator and a parametric 3D face model during adversarial training of the 3D-aware GAN.
no code implementations • 9 Sep 2022 • Ziyu Wang, Yu Deng, Jiaolong Yang, Jingyi Yu, Xin Tong
Experiments show that our method can successfully learn the generative model from unstructured monocular images and well disentangle the shape and appearance for objects (e. g., chairs) with large topological variance.
no code implementations • ICCV 2023 • Jianfeng Xiang, Jiaolong Yang, Yu Deng, Xin Tong
This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024X1024) while keeping strict 3D consistency as in volume rendering.
no code implementations • CVPR 2022 • Yu Deng, Jiaolong Yang, Jianfeng Xiang, Xin Tong
3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses.
no code implementations • 31 Oct 2021 • Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi, Stephen Xia, Yu Deng, Nabil Alshurafa
Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives.
no code implementations • 19 Mar 2021 • Yun Zhao, Qinghang Hong, Xinlu Zhang, Yu Deng, Yuqing Wang, Linda Petzold
However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes.
no code implementations • 3 Feb 2021 • Yu Deng, Ling Wang, Chen Zhao, Shaojie Tang, Xiaoguang Cheng, Hong-Wen Deng, Weihua Zhou
In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.
no code implementations • 4 Dec 2020 • Yiou Lin, Hang Lei, Yu Deng
The search frequency of the rumor is used as an observation variable of new insiders.
1 code implementation • CVPR 2021 • Yu Deng, Jiaolong Yang, Xin Tong
We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes.
1 code implementation • NAACL 2021 • Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, Meng Jiang
In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains.
1 code implementation • EMNLP 2020 • Wenhao Yu, Lingfei Wu, Yu Deng, Ruchi Mahindru, Qingkai Zeng, Sinem Guven, Meng Jiang
In recent years, the need for community technical question-answering sites has increased significantly.
no code implementations • ACL 2020 • Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang
Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.
1 code implementation • CVPR 2020 • Sicheng Xu, Jiaolong Yang, Dong Chen, Fang Wen, Yu Deng, Yunde Jia, Xin Tong
We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images.
4 code implementations • CVPR 2020 • Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, Xin Tong
Our method can also be used to embed real images into the disentangled latent space.
no code implementations • 2 Apr 2019 • Prakash Adekkanattu, Guoqian Jiang, Yuan Luo, Paul R. Kingsbury, Zhen-Xing Xu, Luke V. Rasmussen, Jennifer A. Pacheco, Richard C. Kiefer, Daniel J. Stone, Pascal S. Brandt, Liang Yao, Yizhen Zhong, Yu Deng, Fei Wang, Jessica S. Ancker, Thomas R. Campion, Jyotishman Pathak
While the NLP system showed high precision and recall measurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.
4 code implementations • 20 Mar 2019 • Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, Xin Tong
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce.
Ranked #3 on 3D Face Reconstruction on Florence (RMSE Cooperative metric)
no code implementations • 15 Nov 2018 • Yizhen Zhong, Luke Rasmussen, Yu Deng, Jennifer Pacheco, Maureen Smith, Justin Starren, Wei-Qi Wei, Peter Speltz, Joshua Denny, Nephi Walton, George Hripcsak, Christopher G. Chute, Yuan Luo
Good classification accuracy with simple features demonstrated the attribution coherence and the feasibility of automatic identification of design patterns.
no code implementations • 13 Jun 2018 • Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.