Search Results for author: Yuan Li

Found 43 papers, 16 papers with code

elBERto: Self-supervised Commonsense Learning for Question Answering

no code implementations17 Mar 2022 Xunlin Zhan, Yuan Li, Xiao Dong, Xiaodan Liang, Zhiting Hu, Lawrence Carin

Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context.

Question Answering Representation Learning

1000x Faster Camera and Machine Vision with Ordinary Devices

no code implementations23 Jan 2022 Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, Jianing Li, Shanshan Jia, Yihua Fu, Boxin Shi, Si Wu, Yonghong Tian

By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.

Object Detection

Enhancing and Dissecting Crowd Counting By Synthetic Data

no code implementations22 Jan 2022 Yi Hou, Chengyang Li, Yuheng Lu, Liping Zhu, Yuan Li, Huizhu Jia, Xiaodong Xie

In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity.

Crowd Counting

BBA-net: A bi-branch attention network for crowd counting

no code implementations22 Jan 2022 Yi Hou, Chengyang Li, Fan Yang, Cong Ma, Liping Zhu, Yuan Li, Huizhu Jia, Xiaodong Xie

Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map.

Crowd Counting

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT

1 code implementation6 Jan 2022 Aparna Elangovan, Yuan Li, Douglas E. V. Pires, Melissa J. Davis, Karin Verspoor

However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision.

TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion

1 code implementation4 Dec 2021 Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu

The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to advocating financial inclusion.

Transfer Learning

Monocular Road Planar Parallax Estimation

no code implementations22 Nov 2021 Haobo Yuan, Teng Chen, Wei Sui, Jiafeng Xie, Lefei Zhang, Yuan Li, Qian Zhang

By warping the consecutive frames using the road plane as a reference, the 3D structure can be estimated from the planar parallax and the residual image displacements.

3D Reconstruction Autonomous Driving

The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations

no code implementations18 Sep 2021 Yanwei Fu, Feng Li, Paula boned Fustel, Lei Zhao, Lijie Jia, Haojie Zheng, Qiang Sun, Shisong Rong, Haicheng Tang, xiangyang xue, Li Yang, Hong Li, Jiao Xie Wenxuan Wang, Yuan Li, Wei Wang, Yantao Pei, Jianmin Wang, Xiuqi Wu, Yanhua Zheng, Hongxia Tian, Mengwei Gu

The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).

SPAN: Subgraph Prediction Attention Network for Dynamic Graphs

no code implementations17 Aug 2021 Yuan Li, Chuanchang Chen, Yubo Tao, Hai Lin

This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction.

Link Prediction

Learning point embedding for 3D data processing

no code implementations19 Jul 2021 Zhenpeng Chen, Yuan Li

Among 2D convolutional networks on point clouds, point-based approaches consume point clouds of fixed size directly.

Rapid COVID-19 Risk Screening by Eye-region Manifestations

no code implementations12 Jun 2021 Yanwei Fu, Lei Zhao, Haojie Zheng, Qiang Sun, Li Yang, Hong Li, Jiao Xie, xiangyang xue, Feng Li, Yuan Li, Wei Wang, Yantao Pei, Jianmin Wang, Xiuqi Wu, Yanhua Zheng, Hongxia Tian Mengwei Gu1

It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.

Refiner: Refining Self-attention for Vision Transformers

1 code implementation7 Jun 2021 Daquan Zhou, Yujun Shi, Bingyi Kang, Weihao Yu, Zihang Jiang, Yuan Li, Xiaojie Jin, Qibin Hou, Jiashi Feng

Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.

Image Classification

Certificate complexity and symmetry of nested canalizing functions

no code implementations10 Feb 2021 Yuan Li, Frank Ingram, Huaming Zhang

For both Boolean values $b\in\{0, 1\}$, we obtain a formula for $b$-certificate complexity and consequently, we develop a direct proof of the certificate complexity formula of an NCF.

Combinatorics Discrete Mathematics

Unusual heat transport of the Kitaev material Na$_2$Co$_2$TeO$_6$: putative quantum spin liquid and low-energy spin excitations

no code implementations28 Jan 2021 Xiaochen Hong, Matthias Gillig, Richard Hentrich, Weiliang Yao, Vilmos Kocsis, Arthur R. Witte, Tino Schreiner, Danny Baumann, Nicolás Pérez, Anja U. B. Wolter, Yuan Li, Bernd Büchner, Christian Hess

We studied the field dependent thermal conductivity ($\kappa$) of Na$_2$Co$_2$TeO$_6$, a compound considered as the manifestation of the Kitaev model based on the high-spin $d^7$ Co$^{2+}$ ions.

Strongly Correlated Electrons

Extremal solution and Liouville theorem for anisotropic elliptic equations

no code implementations4 Jan 2021 Yuan Li

We study the quasilinear Dirichlet boundary problem \begin{equation}\nonumber \left\{ \begin{aligned} -Qu&=\lambda e^{u} \quad \mbox{in}\quad\Omega\\ u&=0 \quad \mbox{on}\quad\partial\Omega,\\ \end{aligned} \right.

Analysis of PDEs

Stacked Homography Transformations for Multi-View Pedestrian Detection

no code implementations ICCV 2021 Liangchen Song, Jialian Wu, Ming Yang, Qian Zhang, Yuan Li, Junsong Yuan

This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views.

Pedestrian Detection

AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation

no code implementations16 Aug 2020 Xinyu Gong, Wuyang Chen, Yifan Jiang, Ye Yuan, Xian-Ming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.

Neural Architecture Search Pose Estimation

AFDet: Anchor Free One Stage 3D Object Detection

4 code implementations23 Jun 2020 Runzhou Ge, Zhuangzhuang Ding, Yihan Hu, Yu Wang, Sijia Chen, Li Huang, Yuan Li

High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.

3D Object Detection Autonomous Driving

FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

1 code implementation21 Jun 2020 Jiemin Fang, Yuzhu Sun, Qian Zhang, Kangjian Peng, Yuan Li, Wenyu Liu, Xinggang Wang

In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e. g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.

Image Classification Neural Architecture Search +3

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems

2 code implementations14 Jun 2020 Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He

This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems.

Federated Learning

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

1 code implementation EMNLP 2020 Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao

We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.

Language Modelling Representation Learning +1

Fast Neural Network Adaptation via Parameter Remapping and Architecture Search

no code implementations ICLR 2020 Jiemin Fang, Yuzhu Sun, Kangjian Peng, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang

In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.

Image Classification Neural Architecture Search +2

FasterSeg: Searching for Faster Real-time Semantic Segmentation

1 code implementation ICLR 2020 Wuyang Chen, Xinyu Gong, Xian-Ming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.

Neural Architecture Search Real-Time Semantic Segmentation

An Image Based Visual Servo Approach with Deep Learning for Robotic Manipulation

no code implementations17 Sep 2019 Jingshu Liu, Yuan Li

With the powerful learning capabilities of convolutional neural networks(CNN), autonomous learning to extract features from images and fitting the nonlinear relationships from image space to task space is achieved, which can greatly facilitate the image based visual servo procedure.

Visual Servo

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

no code implementations23 Jul 2019 Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He

By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.

Federated Learning

Object Detection in Video with Spatial-temporal Context Aggregation

no code implementations11 Jul 2019 Hao Luo, Lichao Huang, Han Shen, Yuan Li, Chang Huang, Xinggang Wang

Without any bells and whistles, our method obtains 80. 3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts.

Frame Video Object Detection

The iMaterialist Fashion Attribute Dataset

1 code implementation13 Jun 2019 Sheng Guo, Weilin Huang, Xiao Zhang, Prasanna Srikhanta, Yin Cui, Yuan Li, Matthew R. Scott, Hartwig Adam, Serge Belongie

The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total.

General Classification Image Classification +1

Joint Learning of Neural Networks via Iterative Reweighted Least Squares

1 code implementation16 May 2019 Zaiwei Zhang, Xiangru Huang, Qi-Xing Huang, Xiao Zhang, Yuan Li

We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.

General Classification Image Classification +1

Graph Transformer

no code implementations ICLR 2019 Yuan Li, Xiaodan Liang, Zhiting Hu, Yinbo Chen, Eric P. Xing

Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.

Few-Shot Learning General Classification +3

Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations

no code implementations24 Feb 2019 Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn

As we show, datasets produced by crowd-sourcing are often not of this type: the data is highly redundantly annotated ($\ge 5$ annotations per instance), and the vast majority of workers produce high quality outputs.

Massively Multilingual Transfer for NER

1 code implementation ACL 2019 Afshin Rahimi, Yuan Li, Trevor Cohn

In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language.

Cross-Lingual Transfer Few-Shot Learning +3

Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks

no code implementations19 Nov 2018 Yuan Li, Yuanjie Yu, Zefeng Li, Yangkun Lin, Meifang Xu, Jiwei Li, Xi Zhou

Recently, semantic segmentation and general object detection frameworks have been widely adopted by scene text detecting tasks.

Object Detection Semantic Segmentation

Computer Analysis of Architecture Using Automatic Image Understanding

no code implementations13 Jul 2018 Fan Wei, Yuan Li, Lior Shamir

Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion.

Graph-based regularization for regression problems with alignment and highly-correlated designs

no code implementations20 Mar 2018 Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman

This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients -- meaning there is \emph{alignment} between the covariates and regression coefficients.

Model Selection

Learning how to Active Learn: A Deep Reinforcement Learning Approach

1 code implementation EMNLP 2017 Meng Fang, Yuan Li, Trevor Cohn

Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate.

Active Learning Named Entity Recognition +1

Learning Unified Embedding for Apparel Recognition

no code implementations19 Jul 2017 Yang Song, Yuan Li, Bo Wu, Chao-Yeh Chen, Xiao Zhang, Hartwig Adam

To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss.

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

no code implementations26 Mar 2017 Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing

Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.

Community Detection with Node Attributes and its Generalization

no code implementations12 Apr 2016 Yuan Li

This new model can recover communities with higher accuracy even when node attributes and communities are uncorre- lated.

Community Detection

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