no code implementations • LREC 2022 • Yue Cui, Junhui Zhu, Liner Yang, Xuezhi Fang, Xiaobin Chen, Yujie Wang, Erhong Yang
The construct of linguistic complexity has been widely used in language learning research.
no code implementations • 13 Sep 2023 • Yujie Wang, Xiangru Xu
When the disturbance input matrix is nonlinear, existing disturbance observer design methods rely on the solvability of a partial differential equation or the existence of an output function with a uniformly well-defined disturbance relative degree, which can pose significant limitations.
no code implementations • 7 Aug 2023 • Aditya G. Parameswaran, Shreya Shankar, Parth Asawa, Naman jain, Yujie Wang
Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone.
no code implementations • 5 Jul 2023 • Yujie Wang, Youhe Jiang, Xupeng Miao, Fangcheng Fu, Xiaonan Nie, Bin Cui
Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models.
no code implementations • 11 May 2023 • Yujie Wang, Chao Huang, Liner Yang, Zhixuan Fang, Yaping Huang, Yang Liu, Erhong Yang
The SES method is designed specifically for sequence labeling tasks.
no code implementations • 16 Feb 2023 • Yujie Wang, Xiangru Xu
This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control-input matrices.
3 code implementations • 25 Nov 2022 • Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui
Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models.
no code implementations • 3 Oct 2022 • Yujie Wang, Xuelin Chen, Baoquan Chen
We present a 3D generative model for general natural scenes.
1 code implementation • 12 Jul 2022 • Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang
Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.
1 code implementation • SemEval (NAACL) 2022 • Cunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, Yaping Huang
This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French.
no code implementations • 16 Apr 2022 • Yujie Wang, Mike Izbicki
We introduce the tree loss as a drop-in replacement for the cross entropy loss.
1 code implementation • 30 Mar 2022 • Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang
Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
no code implementations • 24 Mar 2022 • Yujie Wang, Xiangru Xu
This work presents a safe control design approach that integrates the disturbance observer (DOB) and the control barrier function (CBF) for systems with external disturbances.
no code implementations • 9 Dec 2021 • Gang Li, Xiang Li, Yujie Wang, Shanshan Zhang, Yichao Wu, Ding Liang
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
no code implementations • CVPR 2022 • Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P. N. Suganthan
We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space.
no code implementations • 24 Nov 2021 • Yujie Wang, Junqin Huang, Mengya Gao, Yichao Wu, Zhenfei Yin, Ding Liang, Junjie Yan
Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.
no code implementations • 16 Nov 2021 • Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
no code implementations • 7 Nov 2021 • Yuxin Tian, Yujie Wang, Ming Ouyang, Xuesong Shi
This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system.
1 code implementation • 6 Jun 2021 • Yixin Zhuang, Yunzhe Liu, Yujie Wang, Baoquan Chen
However, the image features for describing 3D point samplings of implicit functions are less effective when significant variations of occlusions, views, and appearances exist from the image.
no code implementations • 31 Mar 2021 • Jiangfan Han, Mengya Gao, Yujie Wang, Quanquan Li, Hongsheng Li, Xiaogang Wang
To solve this problem, in this paper, we propose a novel student-dependent distillation method, knowledge consistent distillation, which makes teacher's knowledge more consistent with the student and provides the best suitable knowledge to different student networks for distillation.
no code implementations • 30 Mar 2021 • Shaopeng Guo, Yujie Wang, Kun Yuan, Quanquan Li
In this paper we propose a novel network adaption method called Differentiable Network Adaption (DNA), which can adapt an existing network to a specific computation budget by adjusting the width and depth in a differentiable manner.
no code implementations • 5 Feb 2021 • Ming Ouyang, Xuesong Shi, Yujie Wang, Yuxin Tian, Yingzhe Shen, Dawei Wang, Peng Wang, Zhiqiang Cao
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots.
no code implementations • 1 Jan 2021 • Yuxin Yue, Quanquan Li, Yujie Wang
Many commonly-used detection frameworks aim to handle the multi-scale object detection problem.
1 code implementation • CVPR 2020 • Shaopeng Guo, Yujie Wang, Quanquan Li, Junjie Yan
In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process.
5 code implementations • 1 Jan 2020 • Jiacheng Li, Yujie Wang, Julian McAuley
Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently.
no code implementations • 8 Dec 2019 • Yingda Yin, Qingnan Fan, Dong-Dong Chen, Yujie Wang, Angelica Aviles-Rivero, Ruoteng Li, Carola-Bibiane Schnlieb, Baoquan Chen
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.
no code implementations • 10 Oct 2019 • Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
no code implementations • 24 Jul 2018 • Yujie Wang, Simon Sun, Jahow Yu, Dr. Limin Yu
As melanoma diagnoses increase across the US, automated efforts to identify malignant lesions become increasingly of interest to the research community.
no code implementations • CVPR 2018 • Guanglu Song, Yu Liu, Ming Jiang, Yujie Wang, Junjie Yan, Biao Leng
Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone.
2 code implementations • ICCV 2017 • Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
Ranked #22 on
Video Object Detection
on ImageNet VID