Search Results for author: Shuang Wu

Found 36 papers, 15 papers with code

ActPerFL: Active Personalized Federated Learning

no code implementations FL4NLP (ACL) 2022 Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.

Personalized Federated Learning

Graph Federated Learning with Hidden Representation Sharing

no code implementations23 Dec 2022 Shuang Wu, Mingxuan Zhang, Yuantong Li, Carl Yang, Pan Li

On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients.

Federated Learning

Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels

no code implementations13 Dec 2022 Shuang Wu, Xiaoqiang Ren, Qing-Shan Jia, Karl Henrik Johansson, Ling Shi

To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle's index theory to design an index-based scheduling policy algorithm.

Decision Making Scheduling

Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods

1 code implementation27 Nov 2022 Zhengjie Huang, Zhenguang Liu, Jianhai Chen, Qinming He, Shuang Wu, Lei Zhu, Meng Wang

Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts.

Retrieval

Federated Learning with Label Distribution Skew via Logits Calibration

1 code implementation1 Sep 2022 Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu

Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.

Federated Learning

Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations

no code implementations25 Jul 2022 Wenjie Pei, Shuang Wu, Dianwen Mei, Fanglin Chen, Jiandong Tian, Guangming Lu

In this work we design a novel knowledge distillation framework to guide the learning of the object detector and thereby restrain the overfitting in both the pre-training stage on base classes and fine-tuning stage on novel classes.

Few-Shot Object Detection Knowledge Distillation +1

Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection

1 code implementation22 Jul 2022 Shuang Wu, Wenjie Pei, Dianwen Mei, Fanglin Chen, Jiandong Tian, Guangming Lu

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy.

Few-Shot Object Detection object-detection

NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition

1 code implementation12 May 2022 Shuang Wu, Xiaoning Song, ZhenHua Feng, Xiao-Jun Wu

To deal with this issue, we advocate a novel lexical enhancement method, InterFormer, that effectively reduces the amount of computational and memory costs by constructing non-flat lattices.

Chinese Named Entity Recognition named-entity-recognition +1

Copy Motion From One to Another: Fake Motion Video Generation

no code implementations3 May 2022 Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang, Lei Zhu, Shuang Wu, Fuli Feng

3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.

Video Generation

Self-Aware Personalized Federated Learning

no code implementations17 Apr 2022 Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.

Personalized Federated Learning

Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation

1 code implementation CVPR 2022 Zhenguang Liu, Runyang Feng, Haoming Chen, Shuang Wu, Yixing Gao, Yunjun Gao, Xiang Wang

State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame).

Pose Estimation

Towards Practical Certifiable Patch Defense with Vision Transformer

no code implementations CVPR 2022 Zhaoyu Chen, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Wenqiang Zhang

To move towards a practical certifiable patch defense, we introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing (DS).

Residual Bootstrap Exploration for Stochastic Linear Bandit

no code implementations23 Feb 2022 Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng

We propose a new bootstrap-based online algorithm for stochastic linear bandit problems.

Federated Learning Challenges and Opportunities: An Outlook

no code implementations1 Feb 2022 Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang

Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs.

Federated Learning

Dual Learning Music Composition and Dance Choreography

no code implementations28 Jan 2022 Shuang Wu, Zhenguang Li, Shijian Lu, Li Cheng

Music and dance have always co-existed as pillars of human activities, contributing immensely to the cultural, social, and entertainment functions in virtually all societies.

Motion Prediction via Joint Dependency Modeling in Phase Space

no code implementations7 Jan 2022 Pengxiang Su, Zhenguang Liu, Shuang Wu, Lei Zhu, Yifang Yin, Xuanjing Shen

In this paper, we introduce a novel convolutional neural model to effectively leverage explicit prior knowledge of motion anatomy, and simultaneously capture both spatial and temporal information of joint trajectory dynamics.

Anatomy motion prediction

Detecting Camouflaged Object in Frequency Domain

1 code implementation CVPR 2022 Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, Shouhong Ding

We first design a novel frequency enhancement module (FEM) to dig clues of camouflaged objects in the frequency domain.

object-detection Object Detection

Towards Efficient Data Free Black-Box Adversarial Attack

1 code implementation CVPR 2022 Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, Chao Wu

The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.

Adversarial Attack

Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction

1 code implementation30 Dec 2021 Zhenguang Liu, Shuang Wu, Shuyuan Jin, Shouling Ji, Qi Liu, Shijian Lu, Li Cheng

One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results.

motion prediction

DENSE: Data-Free One-Shot Federated Learning

1 code implementation23 Dec 2021 Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu

One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.

Federated Learning

Music-to-Dance Generation with Optimal Transport

no code implementations3 Dec 2021 Shuang Wu, Shijian Lu, Li Cheng

We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music.

Retrieval Unity

Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game

no code implementations ICLR 2022 Haobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang, Kai Li, Junliang Xing, Bin Li, Bo Ma, Qiang Fu, Yang Wei

The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.

Policy Gradient Methods

MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition

1 code implementation ACL 2021 Shuang Wu, Xiaoning Song, ZhenHua Feng

This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters.

Chinese Named Entity Recognition named-entity-recognition +1

Aggregated Multi-GANs for Controlled 3D Human Motion Prediction

no code implementations17 Mar 2021 Zhenguang Liu, Kedi Lyu, Shuang Wu, Haipeng Chen, Yanbin Hao, Shouling Ji

Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways.

Human motion prediction motion prediction

Motion Prediction Using Trajectory Cues

1 code implementation ICCV 2021 Zhenguang Liu, Pengxiang Su, Shuang Wu, Xuanjing Shen, Haipeng Chen, Yanbin Hao, Meng Wang

Predicting human motion from a historical pose sequence is at the core of many applications in computer vision.

motion prediction

Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers

2 code implementations5 Sep 2019 Yukuan Yang, Shuang Wu, Lei Deng, Tianyi Yan, Yuan Xie, Guoqi Li

In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency.

Quantization

Convolution with even-sized kernels and symmetric padding

1 code implementation NeurIPS 2019 Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi

Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process.

Continual Learning Image Classification

Robust and Efficient Graph Correspondence Transfer for Person Re-identification

no code implementations15 May 2018 Qin Zhou, Heng Fan, Hua Yang, Hang Su, Shibao Zheng, Shuang Wu, Haibin Ling

To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.

Graph Matching Person Re-Identification

L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks

no code implementations27 Feb 2018 Shuang Wu, Guoqi Li, Lei Deng, Liu Liu, Yuan Xie, Luping Shi

Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs).

Quantization

Training and Inference with Integers in Deep Neural Networks

2 code implementations ICLR 2018 Shuang Wu, Guoqi Li, Feng Chen, Luping Shi

Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics.

Continual Learning

Slim Embedding Layers for Recurrent Neural Language Models

no code implementations27 Nov 2017 Zhongliang Li, Raymond Kulhanek, Shaojun Wang, Yunxin Zhao, Shuang Wu

When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models.

Language Modelling

Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts

no code implementations CVPR 2014 Shuang Wu, Sravanthi Bondugula, Florian Luisier, Xiaodan Zhuang, Pradeep Natarajan

Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples.

Event Detection

A unified model of short-range and long-range motion perception

no code implementations NeurIPS 2010 Shuang Wu, Xuming He, Hongjing Lu, Alan L. Yuille

The human vision system is able to effortlessly perceive both short-range and long-range motion patterns in complex dynamic scenes.

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