Search Results for author: Shuang Yang

Found 35 papers, 14 papers with code

UniCon+: ICTCAS-UCAS Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2022

no code implementations22 Jun 2022 Yuanhang Zhang, Susan Liang, Shuang Yang, Shiguang Shan

This report presents a brief description of our winning solution to the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2022.

UniCon: Unified Context Network for Robust Active Speaker Detection

no code implementations5 Aug 2021 Yuanhang Zhang, Susan Liang, Shuang Yang, Xiao Liu, Zhongqin Wu, Shiguang Shan, Xilin Chen

Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties.

Audio-Visual Active Speaker Detection

Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation

1 code implementation10 Jun 2021 Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li

In this paper, we show that such efficiency highly depends on the scale at which the attack is applied, and attacking at the optimal scale significantly improves the efficiency.

Face Recognition

Learning to Schedule DAG Tasks

no code implementations5 Mar 2021 Zhigang Hua, Feng Qi, Gan Liu, Shuang Yang

Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity.

Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

1 code implementation25 Feb 2021 Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li

We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space.

Learn an Effective Lip Reading Model without Pains

1 code implementation15 Nov 2020 Dalu Feng, Shuang Yang, Shiguang Shan, Xilin Chen

Considering the non-negligible effects of these strategies and the existing tough status to train an effective lip reading model, we perform a comprehensive quantitative study and comparative analysis, for the first time, to show the effects of several different choices for lip reading.

 Ranked #1 on Lipreading on CAS-VSR-W1k (LRW-1000) (using extra training data)

Lipreading Lip Reading +1

Learning (Re-)Starting Solutions for Vehicle Routing Problems

no code implementations8 Aug 2020 Xingwen Zhang, Shuang Yang

A key challenge in solving a combinatorial optimization problem is how to guide the agent (i. e., solver) to efficiently explore the enormous search space.

Combinatorial Optimization

Bandit Samplers for Training Graph Neural Networks

2 code implementations NeurIPS 2020 Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi

However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).

Graph Attention

Variational Optimization for the Submodular Maximum Coverage Problem

no code implementations10 Jun 2020 Jian Du, Zhigang Hua, Shuang Yang

We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications.

QEBA: Query-Efficient Boundary-Based Blackbox Attack

no code implementations CVPR 2020 Huichen Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li

Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction.

Autonomous Driving Dimensionality Reduction

Synchronous Bidirectional Learning for Multilingual Lip Reading

1 code implementation8 May 2020 Mingshuang Luo, Shuang Yang, Xilin Chen, Zitao Liu, Shiguang Shan

Based on this idea, we try to explore the synergized learning of multilingual lip reading in this paper, and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.

Lip Reading

A Learning-based Iterative Method for Solving Vehicle Routing Problems

1 code implementation ICLR 2020 Hao Lu, Xingwen Zhang, Shuang Yang

This paper is concerned with solving combinatorial optimization problems, in particular, the capacitated vehicle routing problems (CVRP).

Combinatorial Optimization

Secret Sharing based Secure Regressions with Applications

no code implementations10 Apr 2020 Chaochao Chen, Liang Li, Wenjing Fang, Jun Zhou, Li Wang, Lei Wang, Shuang Yang, Alex Liu, Hao Wang

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.

Mutual Information Maximization for Effective Lip Reading

1 code implementation13 Mar 2020 Xing Zhao, Shuang Yang, Shiguang Shan, Xilin Chen

By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading.

Lipreading Lip Reading

Deformation Flow Based Two-Stream Network for Lip Reading

1 code implementation12 Mar 2020 Jing-Yun Xiao, Shuang Yang, Yuan-Hang Zhang, Shiguang Shan, Xilin Chen

Observing on the continuity in adjacent frames in the speaking process, and the consistency of the motion patterns among different speakers when they pronounce the same phoneme, we model the lip movements in the speaking process as a sequence of apparent deformations in the lip region.

Knowledge Distillation Lipreading +1

Industrial Scale Privacy Preserving Deep Neural Network

no code implementations11 Mar 2020 Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang, Jun Zhou, Shuang Yang

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.

Fraud Detection Privacy Preserving

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

no code implementations10 Mar 2020 Yankun Ren, Jianbin Lin, Siliang Tang, Jun Zhou, Shuang Yang, Yuan Qi, Xiang Ren

It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.

Adversarial Text General Classification +3

Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading

no code implementations9 Mar 2020 Mingshuang Luo, Shuang Yang, Shiguang Shan, Xilin Chen

On the one hand, we introduce the evaluation metric (refers to the character error rate in this paper) as a form of reward to optimize the model together with the original discriminative target.

Lipreading Lip Reading

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

1 code implementation28 Feb 2020 Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi

Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.

Fraud Detection

Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing

no code implementations27 Feb 2020 Ziqi Liu, Dong Wang, Qianyu Yu, Zhiqiang Zhang, Yue Shen, Jian Ma, Wenliang Zhong, Jinjie Gu, Jun Zhou, Shuang Yang, Yuan Qi

In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.

Graph Representation Learning

Uncovering Insurance Fraud Conspiracy with Network Learning

no code implementations27 Feb 2020 Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi

In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.

Fraud Detection Graph Learning

Privacy Preserving PCA for Multiparty Modeling

no code implementations6 Feb 2020 Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang, Jun Zhou, Guiquan Liu, Shuang Yang

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.

Fraud Detection Privacy Preserving

Solving Billion-Scale Knapsack Problems

no code implementations2 Feb 2020 Xingwen Zhang, Feng Qi, Zhigang Hua, Shuang Yang

Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale.

Distributed Computing

Signet Ring Cell Detection With a Semi-supervised Learning Framework

1 code implementation9 Jul 2019 Jiahui Li, Shuang Yang, Xiaodi Huang, Qian Da, Xiaoqun Yang, Zhiqiang Hu, Qi Duan, Chaofu Wang, Hongsheng Li

Our framework achieves accurate signet ring cell detection and can be readily applied in the clinical trails.

Accurate Nuclear Segmentation with Center Vector Encoding

no code implementations9 Jul 2019 Jiahui Li, Zhiqiang Hu, Shuang Yang

Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion.

Nuclear Segmentation

Enhanced Network Embedding with Text Information

2 code implementations 24th International Conference on Pattern Recognition (ICPR) 2018 Shuang Yang, Bo Yang

TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information.

Multi-class Classification Network Embedding +1

LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild

2 code implementations16 Oct 2018 Shuang Yang, Yuan-Hang Zhang, Dalu Feng, Mingmin Yang, Chenhao Wang, Jing-Yun Xiao, Keyu Long, Shiguang Shan, Xilin Chen

It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up.

Lipreading Lip Reading +1

Multi-task Sparse Learning with Beta Process Prior for Action Recognition

no code implementations CVPR 2013 Chunfeng Yuan, Weiming Hu, Guodong Tian, Shuang Yang, Haoran Wang

In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible.

Action Recognition Sparse Learning

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