Search Results for author: Shuang Li

Found 54 papers, 28 papers with code

Temporal Logic Point Processes

no code implementations ICML 2020 Shuang Li, Lu Wang, Ruizhi Zhang, xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song

We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.

Point Processes Temporal Logic

Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions

1 code implementation ICCV 2021 Shuang Li, Yilun Du, Antonio Torralba, Josef Sivic, Bryan Russell

Our task poses unique challenges as a system does not know what types of human-object interactions are present in a video or the actual spatiotemporal location of the human and the object.

Human-Object Interaction Detection

Semantic Concentration for Domain Adaptation

1 code implementation ICCV 2021 Shuang Li, Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, Wei Li

To tackle this issue, we propose Semantic Concentration for Domain Adaptation (SCDA), which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions.

Domain Adaptation Transfer Learning

ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach

no code implementations8 Aug 2021 Qi Wen, Shuang Li, Bingfeng Han, Yi Yuan

Chinese character style transfer is a very challenging problem because of the complexity of the glyph shapes or underlying structures and large numbers of existed characters, when comparing with English letters.

Font Generation Style Transfer

I2V-GAN: Unpaired Infrared-to-Visible Video Translation

1 code implementation2 Aug 2021 Shuang Li, Bingfeng Han, Zhenjie Yu, Chi Harold Liu, Kai Chen, Shuigen Wang

Human vision is often adversely affected by complex environmental factors, especially in night vision scenarios.

Object Detection Translation

Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View

no code implementations24 Jun 2021 Shuang Li, Lu Wang, Xinyun Chen, Yixiang Fang, Yan Song

In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease.

Imitation Learning Point Processes

Quotient Space-Based Keyword Retrieval in Sponsored Search

no code implementations26 May 2021 Yijiang Lian, Shuang Li, Chaobing Feng, Yanfeng Zhu

Since the synonymous relations between queries and keywords are quite scarce, the traditional information retrieval framework is inefficient in this scenario.

Information Retrieval

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

no code implementations17 May 2021 Lu Wang, xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei zhang, Xiaofeng He, Le Song, Jingren Zhou, Hongxia Yang

Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.

Contrastive Learning Graph Learning +2

Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation

1 code implementation11 May 2021 Shuang Li, Binhui Xie, Bin Zang, Chi Harold Liu, Xinjing Cheng, Ruigang Yang, Guoren Wang

Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains.

Self-Supervised Learning Semantic Segmentation

Dynamic Domain Adaptation for Efficient Inference

1 code implementation CVPR 2021 Shuang Li, Jinming Zhang, Wenxuan Ma, Chi Harold Liu, Wei Li

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy.

Domain Generalization Transfer Learning

Generalized Domain Conditioned Adaptation Network

1 code implementation23 Mar 2021 Shuang Li, Binhui Xie, Qiuxia Lin, Chi Harold Liu, Gao Huang, Guoren Wang

Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision.

Domain Adaptation

Transferable Semantic Augmentation for Domain Adaptation

1 code implementation CVPR 2021 Shuang Li, Mixue Xie, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Wei Li

To remedy this, we propose a Transferable Semantic Augmentation (TSA) approach to enhance the classifier adaptation ability through implicitly generating source features towards target semantics.

Domain Adaptation

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

1 code implementation CVPR 2021 Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng

Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes.

Data Augmentation Meta-Learning

Digital Beamforming Robust to Time-Varying Carrier Frequency Offset

no code implementations8 Mar 2021 Shuang Li, Payam Nayeri, Michael B. Wakin

We present novel beamforming algorithms that are robust to signal corruptions arising from this time-variant carrier frequency offset.

One-shot Face Reenactment Using Appearance Adaptive Normalization

no code implementations8 Feb 2021 Guangming Yao, Yi Yuan, Tianjia Shao, Shuang Li, Shanqi Liu, Yong liu, Mengmeng Wang, Kun Zhou

The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance.

Face Reenactment

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

1 code implementation13 Dec 2020 Shuang Li, Fangrui Lv, Binhui Xie, Chi Harold Liu, Jian Liang, Chen Qin

Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability.

Semantic Segmentation

Improved Contrastive Divergence Training of Energy Based Models

2 code implementations2 Dec 2020 Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch

Contrastive divergence is a popular method of training energy-based models, but is known to have difficulties with training stability.

Data Augmentation Image Generation

Compositional Visual Generation with Energy Based Models

no code implementations NeurIPS 2020 Yilun Du, Shuang Li, Igor Mordatch

A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge.

Energy-Based Models for Continual Learning

1 code implementation24 Nov 2020 Shuang Li, Yilun Du, Gido M. van de Ven, Igor Mordatch

We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems.

Continual Learning

SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition

1 code implementation CVPR 2021 Yan Xia, Yusheng Xu, Shuang Li, Rui Wang, Juan Du, Daniel Cremers, Uwe Stilla

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors.

Metric Learning Point Cloud Retrieval +1

Domain Agnostic Learning for Unbiased Authentication

no code implementations11 Oct 2020 Jian Liang, Yuren Cao, Shuang Li, Bing Bai, Hao Li, Fei Wang, Kun Bai

We further extend our method to a meta-learning framework to pursue more thorough domain-difference elimination.

Face Recognition Meta-Learning +1

Optimizing AD Pruning of Sponsored Search with Reinforcement Learning

no code implementations5 Aug 2020 Yijiang Lian, Zhijie Chen, Xin Pei, Shuang Li, Yifei Wang, Yuefeng Qiu, Zhiheng Zhang, Zhipeng Tao, Liang Yuan, Hanju Guan, Kefeng Zhang, Zhigang Li, Xiaochun Liu

Industrial sponsored search system (SSS) can be logically divided into three modules: keywords matching, ad retrieving, and ranking.

Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation

1 code implementation4 Aug 2020 Shuang Li, Binhui Xie, Jiashu Wu, Ying Zhao, Chi Harold Liu, Zhengming Ding

In this paper, we propose a Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains.

Domain Adaptation

Domain Conditioned Adaptation Network

1 code implementation14 May 2020 Shuang Li, Chi Harold Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang

Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target.

Domain Adaptation

Compositional Visual Generation and Inference with Energy Based Models

no code implementations13 Apr 2020 Yilun Du, Shuang Li, Igor Mordatch

A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge.

Deep Residual Correction Network for Partial Domain Adaptation

1 code implementation10 Apr 2020 Shuang Li, Chi Harold Liu, Qiuxia Lin, Qi Wen, Limin Su, Gao Huang, Zhengming Ding

Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain.

Partial Domain Adaptation

A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU

1 code implementation11 Mar 2020 Shuang Li, Jiaxi Jiang, Philipp Ruppel, Hongzhuo Liang, Xiaojian Ma, Norman Hendrich, Fuchun Sun, Jianwei Zhang

In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking method.

Image-to-Image Translation Translation

Robust Robotic Pouring using Audition and Haptics

1 code implementation29 Feb 2020 Hongzhuo Liang, Chuangchuang Zhou, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Fuchun Sun, Marcus Stoffel, Jianwei Zhang

Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment.

Iterative Hard Thresholding for Low CP-rank Tensor Models

no code implementations22 Aug 2019 Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell, Jing Qin

In this paper, we utilize the same tensor version of the Restricted Isometry Property (RIP) to extend these results for tensors with low CANDECOMP/PARAFAC (CP) rank.

The Landscape of Non-convex Empirical Risk with Degenerate Population Risk

no code implementations NeurIPS 2019 Shuang Li, Gongguo Tang, Michael B. Wakin

We also apply the theory to matrix sensing and phase retrieval to demonstrate how to infer the landscape of empirical risk from that of the corresponding population risk.

Matrix Completion

Imitation Learning of Neural Spatio-Temporal Point Processes

1 code implementation13 Jun 2019 Shixiang Zhu, Shuang Li, Zhigang Peng, Yao Xie

We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinforcement learning) based approach for model fitting.

Imitation Learning Point Processes

An Approach for Process Model Extraction By Multi-Grained Text Classification

1 code implementation16 May 2019 Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shuang Li, Jian-Min Wang

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.

Classification General Classification +4

Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring

1 code implementation2 Mar 2019 Hongzhuo Liang, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Jianwei Zhang

PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task.

Robotics Sound Audio and Speech Processing

Learning Temporal Point Processes via Reinforcement Learning

no code implementations NeurIPS 2018 Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.

Point Processes

Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network

4 code implementations17 Sep 2018 Shuang Li, Xiaojian Ma, Hongzhuo Liang, Michael Görner, Philipp Ruppel, Bing Fang, Fuchun Sun, Jianwei Zhang

In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands.

Robotics

PointNetGPD: Detecting Grasp Configurations from Point Sets

4 code implementations17 Sep 2018 Hongzhuo Liang, Xiaojian Ma, Shuang Li, Michael Görner, Song Tang, Bin Fang, Fuchun Sun, Jianwei Zhang

In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.

Robotics

Question-Guided Hybrid Convolution for Visual Question Answering

no code implementations ECCV 2018 Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven Hoi, Xiaogang Wang

Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features. To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage.

Question Answering Visual Question Answering

Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification

no code implementations CVPR 2018 Shuang Li, Slawomir Bak, Peter Carr, Xiaogang Wang

As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence.

Video-Based Person Re-Identification

Can Image Retrieval help Visual Saliency Detection?

no code implementations24 Sep 2017 Shuang Li, Peter Mathews

We propose a novel image retrieval framework for visual saliency detection using information about salient objects contained within bounding box annotations for similar images.

Image Retrieval Saliency Detection

Fake News Mitigation via Point Process Based Intervention

no code implementations ICML 2017 Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.

Person Search with Natural Language Description

1 code implementation CVPR 2017 Shuang Li, Tong Xiao, Hongsheng Li, Bolei Zhou, Dayu Yue, Xiaogang Wang

Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance.

Person Search Text based Person Retrieval

Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits

no code implementations14 Oct 2016 Shuang Li, Yao Xie, Le Song

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence.

Change Point Detection

Detecting weak changes in dynamic events over networks

no code implementations29 Mar 2016 Shuang Li, Yao Xie, Mehrdad Farajtabar, Apurv Verma, Le Song

Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics.

Change Point Detection Point Processes

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

no code implementations NeurIPS 2015 Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time.

M-Statistic for Kernel Change-Point Detection

no code implementations NeurIPS 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning.

Change Point Detection

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

1 code implementation NeurIPS 2015 Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it.

Scan $B$-Statistic for Kernel Change-Point Detection

no code implementations5 Jul 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

A novel theoretical result of the paper is the characterization of the tail probability of these statistics using the change-of-measure technique, which focuses on characterizing the tail of the detection statistics rather than obtaining its asymptotic distribution under the null distribution.

Change Point Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.