Search Results for author: Jun Wang

Found 301 papers, 92 papers with code

Detecting Health Advice in Medical Research Literature

1 code implementation EMNLP 2021 Yingya Li, Jun Wang, Bei Yu

We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.

Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit

no code implementations25 Jan 2022 Jianwei Xu, Ran Zhao, Yizhou Yu, Qingwei Zhang, Xianzhang Bian, Jun Wang, Zhizheng Ge, Dahong Qian

In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information.

SSIM Style Transfer

Chinese Word Segmentation with Heterogeneous Graph Neural Network

no code implementations22 Jan 2022 Xuemei Tang, Jun Wang, Qi Su

In recent years, deep learning has achieved significant success in the Chinese word segmentation (CWS) task.

Chinese Word Segmentation Language Modelling

Debiased Recommendation with User Feature Balancing

no code implementations16 Jan 2022 Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen

To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.

Causal Inference Recommendation Systems

Learning to Identify Top Elo Ratings: A Dueling Bandits Approach

1 code implementation12 Jan 2022 Xue Yan, Yali Du, Binxin Ru, Jun Wang, Haifeng Zhang, Xu Chen

The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players.

Differentiable and Scalable Generative Adversarial Models for Data Imputation

no code implementations10 Jan 2022 Yangyang Wu, Jun Wang, Xiaoye Miao, Wenjia Wang, Jianwei Yin

DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.

Imputation

Data-Free Knowledge Transfer: A Survey

no code implementations31 Dec 2021 Yuang Liu, Wei zhang, Jun Wang, Jianyong Wang

In this paper, we provide a comprehensive survey on data-free knowledge transfer from the perspectives of knowledge distillation and unsupervised domain adaptation, to help readers have a better understanding of the current research status and ideas.

Knowledge Distillation Model Compression +2

Settling the Bias and Variance of Meta-Gradient Estimation for Meta-Reinforcement Learning

no code implementations31 Dec 2021 Bo Liu, Xidong Feng, Haifeng Zhang, Jun Wang, Yaodong Yang

In recent years, gradient based Meta-RL (GMRL) methods have achieved remarkable successes in either discovering effective online hyperparameter for one single task (Xu et al., 2018) or learning good initialisation for multi-task transfer learning (Finn et al., 2017).

Meta Reinforcement Learning Transfer Learning

Superpixel-Based Building Damage Detection from Post-earthquake Very High Resolution Imagery Using Deep Neural Networks

no code implementations9 Dec 2021 Jun Wang, Zhoujing Li, Yixuan Qiao, Qiming Qin, Peng Gao, Guotong Xie

This paper presents a novel superpixel based approach combining DNN and a modified segmentation method, to detect damaged buildings from VHR imagery.

Denoising Semantic Similarity +1

Understanding Square Loss in Training Overparametrized Neural Network Classifiers

no code implementations7 Dec 2021 Tianyang Hu, Jun Wang, Wenjia Wang, Zhenguo Li

Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.

Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks

1 code implementation6 Dec 2021 Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang, Jun Wang, Bo Xu

In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL.

Offline RL SMAC +2

Neural Auto-Curricula in Two-Player Zero-Sum Games

1 code implementation NeurIPS 2021 Xidong Feng, Oliver Slumbers, Ziyu Wan, Bo Liu, Stephen Mcaleer, Ying Wen, Jun Wang, Yaodong Yang

When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.

Multi-agent Reinforcement Learning

Learning State Representations via Retracing in Reinforcement Learning

no code implementations24 Nov 2021 Changmin Yu, Dong Li, Jianye Hao, Jun Wang, Neil Burgess

We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks.

Continuous Control Model-based Reinforcement Learning +1

BOiLS: Bayesian Optimisation for Logic Synthesis

no code implementations11 Nov 2021 Antoine Grosnit, Cedric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou Ammar

Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces.

Bayesian Optimisation

MetaMIML: Meta Multi-Instance Multi-Label Learning

no code implementations7 Nov 2021 Yuanlin Yang, Guoxian Yu, Jun Wang, Lei Liu, Carlotta Domeniconi, Maozu Guo

Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances.

Meta-Learning Multi-Label Learning +1

Dispensed Transformer Network for Unsupervised Domain Adaptation

no code implementations28 Oct 2021 Yunxiang Li, Jingxiong Li, Ruilong Dan, Shuai Wang, Kai Jin, Guodong Zeng, Jun Wang, Xiangji Pan, Qianni Zhang, Huiyu Zhou, Qun Jin, Li Wang, Yaqi Wang

To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.

Unsupervised Domain Adaptation

A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers

no code implementations28 Oct 2021 Chenguang Wang, Yaodong Yang, Oliver Slumbers, Congying Han, Tiande Guo, Haifeng Zhang, Jun Wang

In this paper, we shed new light on the generalization ability of deep learning-based solvers for Traveling Salesman Problems (TSP).

DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention

no code implementations27 Oct 2021 David Mguni, Joel Jennings, Taher Jafferjee, Aivar Sootla, Yaodong Yang, Changmin Yu, Usman Islam, Ziyan Wang, Jun Wang

The core of DESTA is a novel game between two RL agents: SAFETY AGENT that is delegated the task of minimising safety violations and TASK AGENT whose goal is to maximise the reward set by the environment task.

Safe Exploration Safe Reinforcement Learning

Measuring the Non-Transitivity in Chess

no code implementations22 Oct 2021 Ricky Sanjaya, Jun Wang, Yaodong Yang

In this paper, we quantify the non-transitivity in Chess through real-world data from human players.

A channel attention based MLP-Mixer network for motor imagery decoding with EEG

no code implementations21 Oct 2021 Yanbin He, Zhiyang Lu, Jun Wang, Jun Shi

Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task.

EEG

Online Markov Decision Processes with Non-oblivious Strategic Adversary

no code implementations7 Oct 2021 Le Cong Dinh, David Henry Mguni, Long Tran-Thanh, Jun Wang, Yaodong Yang

In this setting, we first demonstrate that MDP-Expert, an existing algorithm that works well with oblivious adversaries can still apply and achieve a policy regret bound of $\mathcal{O}(\sqrt{T \log(L)}+\tau^2\sqrt{ T \log(|A|)})$ where $L$ is the size of adversary's pure strategy set and $|A|$ denotes the size of agent's action space.

Multi-Agent Constrained Policy Optimisation

1 code implementation6 Oct 2021 Shangding Gu, Jakub Grudzien Kuba, Munning Wen, Ruiqing Chen, Ziyan Wang, Zheng Tian, Jun Wang, Alois Knoll, Yaodong Yang

To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.

Multi-agent Reinforcement Learning

GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

1 code implementation30 Sep 2021 Yunxiang Li, Shuai Wang, Jun Wang, Guodong Zeng, Wenjun Liu, Qianni Zhang, Qun Jin, Yaqi Wang

In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation.

Performance-Guaranteed ODE Solvers with Complexity-Informed Neural Networks

no code implementations NeurIPS Workshop DLDE 2021 Feng Zhao, Xiang Chen, Jun Wang, Zuoqiang Shi, Shao-Lun Huang

Traditionally, we provide technical parameters for ODE solvers, such as the order, the stepsize and the local error threshold.

Cross Attention-guided Dense Network for Images Fusion

no code implementations23 Sep 2021 Zhengwen Shen, Jun Wang, Zaiyu Pan, Yulian Li, Jiangyu Wang

In this paper, we propose a novel cross attention-guided image fusion network, which is a unified and unsupervised framework for multi-modal image fusion, multi-exposure image fusion, and multi-focus image fusion.

Multi-Exposure Image Fusion

Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics

no code implementations20 Sep 2021 Yixin Wu, Rui Luo, Chen Zhang, Jun Wang, Yaodong Yang

In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers.

Recommendation Fairness: From Static to Dynamic

no code implementations5 Sep 2021 Dell Zhang, Jun Wang

Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem.

Fairness Recommendation Systems

On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games

no code implementations4 Sep 2021 Xiaotie Deng, Yuhao Li, David Henry Mguni, Jun Wang, Yaodong Yang

Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions.

Multi-agent Reinforcement Learning

Top-N Recommendation with Counterfactual User Preference Simulation

no code implementations2 Sep 2021 Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, Jun Wang

To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.

Causal Inference Recommendation Systems

Bilateral Denoising Diffusion Models

no code implementations26 Aug 2021 Max W. Y. Lam, Jun Wang, Rongjie Huang, Dan Su, Dong Yu

In this paper, we propose novel bilateral denoising diffusion models (BDDMs), which take significantly fewer steps to generate high-quality samples.

Denoising

PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refining

no code implementations25 Aug 2021 Jun Wang, Hefeng Zhou, Xiaohan Yu

There are two main problems hindering the performance of the two-phase WSOD approaches, i. e., insufficient learning problem and strict reliance between the FSD and the pseudo ground truth (PGT) generated by theWSOD model.

Weakly Supervised Object Detection

CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation

1 code implementation24 Aug 2021 Xidong Feng, Chen Chen, Dong Li, Mengchen Zhao, Jianye Hao, Jun Wang

Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.

Meta-Learning Recommendation Systems

Settling the Variance of Multi-Agent Policy Gradients

1 code implementation NeurIPS 2021 Jakub Grudzien Kuba, Muning Wen, Yaodong Yang, Linghui Meng, Shangding Gu, Haifeng Zhang, David Henry Mguni, Jun Wang

In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents.

Starcraft

PAC Learnability of Approximate Nash Equilibrium in Bimatrix Games

no code implementations17 Aug 2021 Zhijian Duan, Dinghuai Zhang, Wenhan Huang, Yali Du, Yaodong Yang, Jun Wang, Xiaotie Deng

Computing Nash equilibrium in bimatrix games is PPAD-hard, and many works have focused on the approximate solutions.

Meta-Learning

A Novel 3D Non-Stationary GBSM for 6G THz Ultra-Massive MIMO Wireless Systems

no code implementations14 Aug 2021 Jun Wang, Cheng-Xiang Wang, Jie Huang, Haiming Wang, Xiqi Gao, Xiaohu You, Yang Hao

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems.

Distributed Learning for Time-varying Networks: A Scalable Design

no code implementations31 Jul 2021 Jian Wang, Yourui Huangfu, Rong Li, Yiqun Ge, Jun Wang

The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence".

Federated Learning

Linking Health News to Research Literature

1 code implementation14 Jul 2021 Jun Wang, Bei Yu

Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news.

Named Entity Recognition

Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning

1 code implementation12 Jul 2021 Jun Wang, Chang Xu, Francisco Guzman, Ahmed El-Kishky, Yuqing Tang, Benjamin I. P. Rubinstein, Trevor Cohn

Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.

Data Poisoning Machine Translation +2

Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows

no code implementations6 Jul 2021 Vincent Moens, Aivar Sootla, Haitham Bou Ammar, Jun Wang

We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available.

EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation

1 code implementation3 Jul 2021 Jun Wang, Yang Zhao, Linglong Qian, Xiaohan Yu, Yongsheng Gao

The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e. g., diabetic, hypertensive and solar retinopathies.

Retinal Vessel Segmentation

Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

no code implementations29 Jun 2021 Zhiyang Lu, Zheng Li, Jun Wang, Jun Shi, Dinggang Shen

To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training.

Self-Supervised Learning

A Game-Theoretic Approach to Multi-Agent Trust Region Optimization

1 code implementation12 Jun 2021 Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration.

Atari Games Multi-agent Reinforcement Learning

Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition

no code implementations8 Jun 2021 Max W. Y. Lam, Jun Wang, Chao Weng, Dan Su, Dong Yu

End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization.

raw waveform Speech Recognition

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

1 code implementation5 Jun 2021 Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Weinan Zhang, Jun Wang

Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.

Atari Games Distributed Computing +1

Neural Auto-Curricula

1 code implementation4 Jun 2021 Xidong Feng, Oliver Slumbers, Ziyu Wan, Bo Liu, Stephen Mcaleer, Ying Wen, Jun Wang, Yaodong Yang

When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.

Multi-agent Reinforcement Learning

CT-Net: Channel Tensorization Network for Video Classification

1 code implementation ICLR 2021 Kunchang Li, Xianhang Li, Yali Wang, Jun Wang, Yu Qiao

It can learn to exploit spatial, temporal and channel attention in a high-dimensional manner, to improve the cooperative power of all the feature dimensions in our CT-Module.

Action Classification Action Recognition

MM-AVS: A Full-Scale Dataset for Multi-modal Summarization

no code implementations NAACL 2021 Xiyan Fu, Jun Wang, Zhenglu Yang

Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks.

Question Answering

Self Promotion in US Congressional Tweets

1 code implementation NAACL 2021 Jun Wang, Kelly Cui, Bei Yu

Prior studies have found that women self-promote less than men due to gender stereotypes.

Learning to Select Cuts for Efficient Mixed-Integer Programming

no code implementations28 May 2021 Zeren Huang, Kerong Wang, Furui Liu, Hui-Ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang

In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.

Multiple Instance Learning

Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems

no code implementations27 May 2021 Xijun Li, Weilin Luo, Mingxuan Yuan, Jun Wang, Jiawen Lu, Jie Wang, Jinhu Lu, Jia Zeng

Our method is entirely data driven and thus adaptive, i. e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically.

Graph Embedding

Learning to Safely Exploit a Non-Stationary Opponent

no code implementations NeurIPS 2021 Zheng Tian, Hang Ren, Yaodong Yang, Yuchen Sun, Ziqi Han, Ian Davies, Jun Wang

On the other hand, overfitting to an opponent (i. e., exploiting only one specific type of opponent) makes the learning player easily exploitable by others.

Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions

no code implementations19 May 2021 Yu Wang, Hejia Luo, Ying Chen, Jun Wang, Rong Li, Bin Wang

Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.

Ordering-Based Causal Discovery with Reinforcement Learning

1 code implementation14 May 2021 Xiaoqiang Wang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao, Jun Wang

It is a long-standing question to discover causal relations among a set of variables in many empirical sciences.

Causal Discovery Variable Selection

Boosting Semi-Supervised Face Recognition with Noise Robustness

1 code implementation10 May 2021 Yuchi Liu, Hailin Shi, Hang Du, Rui Zhu, Jun Wang, Liang Zheng, Tao Mei

This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.

Face Recognition

Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images

no code implementations7 May 2021 Yiming Bao, Jun Wang, Tong Li, Linyan Wang, Jianwei Xu, Juan Ye, Dahong Qian

Specifically, the encoder of a DL model that is pre-trained on the source domain is used to initialize the encoder of a reconstruction model.

Domain Adaptation Transfer Learning

An effective self-supervised framework for learning expressive molecular global representations to drug discovery

1 code implementation Briefings in Bioinformatics 2021 Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, Sen Song

In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level.

Drug Discovery

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy

1 code implementation2 May 2021 Yunxiang Li, Guodong Zeng, Yifan Zhang, Jun Wang, Qianni Zhang, Qun Jin, Lingling Sun, Qisi Lian, Neng Xia, Ruizi Peng, Kai Tang, Yaqi Wang, Shuai Wang

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.

General Classification

A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surfaces with Practical Phase Shifts

no code implementations25 Apr 2021 Yingzhuo Sun, Cheng-Xiang Wang, Jie Huang, Jun Wang

In this paper, a three-dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing practical discrete intelligent reflecting surface (IRS) is proposed.

M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

1 code implementation24 Apr 2021 Tianrui Guan, Jun Wang, Shiyi Lan, Rohan Chandra, Zuxuan Wu, Larry Davis, Dinesh Manocha

We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.

3D Object Detection

A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems

no code implementations20 Apr 2021 Jun Wang, Cheng-Xiang Wang, Jie Huang, Haiming Wang, Xiqi Gao

The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users.

An Adversarial Imitation Click Model for Information Retrieval

1 code implementation13 Apr 2021 Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, Yong Yu

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.

Imitation Learning Information Retrieval +1

A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC

no code implementations5 Apr 2021 Jiyuan Hu, Jun Wang, Guangyu Zhong, Jian Cao, Ren Mao, Fan Liang

The reference frame memory accesses in inter prediction result in high DRAM bandwidth requirement and power consumption.

Texture Classification

Two-phase weakly supervised object detection with pseudo ground truth mining

no code implementations1 Apr 2021 Jun Wang

We explore the effectiveness of some representative detectors utilized as the second-phase detector in two-phase WSOD and propose a two-phase WSOD architecture.

Weakly Supervised Object Detection

Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

1 code implementation CVPR 2021 Jiahui She, Yibo Hu, Hailin Shi, Jun Wang, Qiu Shen, Tao Mei

Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity.

Facial Expression Recognition

Source-Free Domain Adaptation for Semantic Segmentation

no code implementations CVPR 2021 Yuang Liu, Wei zhang, Jun Wang

To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation.

Self-Supervised Learning Semantic Segmentation +2

Zero-shot Adversarial Quantization

no code implementations CVPR 2021 Yuang Liu, Wei zhang, Jun Wang

To address the above issues, we propose a zero-shot adversarial quantization (ZAQ) framework, facilitating effective discrepancy estimation and knowledge transfer from a full-precision model to its quantized model.

Quantization Transfer Learning

Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images

no code implementations24 Mar 2021 Yishan He, Fei Gao, Jun Wang, Amir Hussain, Erfu Yang, Huiyu Zhou

In this paper, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings.

A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics

no code implementations23 Mar 2021 Hongru Liang, Haozheng Wang, Qian Li, Jun Wang, Guandong Xu, Jiawei Chen, Jin-Mao Wei, Zhenglu Yang

Learning and analyzing rap lyrics is a significant basis for many web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web.

Information Retrieval Music Information Retrieval +1

Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks

no code implementations22 Mar 2021 Jian Wang, Chen Xu, Rong Li, Yiqun Ge, Jun Wang

We not only verify the performance gain achieved, but also provide implementation-friend designs, i. e., a scalable neural network design for the agent and a virtual environment training framework.

Fairness

Learning to Shape Rewards using a Game of Two Partners

no code implementations16 Mar 2021 David Mguni, Jianhong Wang, Taher Jafferjee, Nicolas Perez-Nieves, Wenbin Song, Yaodong Yang, Feifei Tong, Hui Chen, Jiangcheng Zhu, Jun Wang

Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.

Modelling Behavioural Diversity for Learning in Open-Ended Games

3 code implementations14 Mar 2021 Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers, David Henry Mguni, Ying Wen, Jun Wang

Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e. g., Rock-Paper-Scissors).

Point Processes

Online Double Oracle

1 code implementation13 Mar 2021 Le Cong Dinh, Yaodong Yang, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Haitham Bou Ammar, Jun Wang

Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence.

High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection

no code implementations7 Mar 2021 Yunxiang Li, Yifan Zhang, Yaqi Wang, Shuai Wang, Ruizi Peng, Kai Tang, Qianni Zhang, Jun Wang, Qun Jin, Lingling Sun

As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.

Semantic Segmentation

Adaptive Multi-Teacher Multi-level Knowledge Distillation

1 code implementation6 Mar 2021 Yuang Liu, Wei zhang, Jun Wang

Knowledge distillation~(KD) is an effective learning paradigm for improving the performance of lightweight student networks by utilizing additional supervision knowledge distilled from teacher networks.

Knowledge Distillation

IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures

no code implementations3 Mar 2021 Jun Wang, Wei, Chen, Daicong Da, Mark Fuge, Rahul Rai

It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from geometry to properties.

Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect

no code implementations2 Mar 2021 Jun Wang, Max W. Y. Lam, Dan Su, Dong Yu

We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference.

Speaker Verification Speech Separation

Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation

2 code implementations1 Mar 2021 Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu

One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throughout all layers.

Speech Separation

Contrastive Separative Coding for Self-supervised Representation Learning

no code implementations1 Mar 2021 Jun Wang, Max W. Y. Lam, Dan Su, Dong Yu

To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC).

Representation Learning Self-Supervised Learning +1

Controllable and Diverse Text Generation in E-commerce

no code implementations23 Feb 2021 Huajie Shao, Jun Wang, Haohong Lin, Xuezhou Zhang, Aston Zhang, Heng Ji, Tarek Abdelzaher

The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy.

Text Generation

Mask Guided Attention For Fine-Grained Patchy Image Classification

2 code implementations4 Feb 2021 Jun Wang, Xiaohan Yu, Yongsheng Gao

Specifically, the proposed MGA integrates a pre-trained semantic segmentation model that produces auxiliary supervision signal, i. e., patchy attention mask, enabling a discriminative representation learning.

General Classification Image Classification +2

A relic sketch extraction framework based on detail-aware hierarchical deep network

no code implementations17 Jan 2021 Jinye Peng, Jiaxin Wang, Jun Wang, Erlei Zhang, Qunxi Zhang, Yongqin Zhang, Xianlin Peng, Kai Yu

For the fine extraction stage, we design a new multiscale U-Net (MSU-Net) to effectively remove disease noise and refine the sketch.

Edge Detection Transfer Learning

Learning the Implicit Semantic Representation on Graph-Structured Data

1 code implementation16 Jan 2021 Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Jun Wang, Mengdi Zhang, Enhong Chen

Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited.

Representation Learning

Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography

no code implementations15 Jan 2021 Ronglin Gong, Jun Wang, Jun Shi

In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammography-based CAD.

General Classification Image Restoration +2

Efficient Semi-Implicit Variational Inference

no code implementations15 Jan 2021 Vincent Moens, Hang Ren, Alexandre Maraval, Rasul Tutunov, Jun Wang, Haitham Ammar

In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI).

Variational Inference

Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks

2 code implementations13 Jan 2021 Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu

Recent research on the time-domain audio separation networks (TasNets) has brought great success to speech separation.

Speech Separation

FaceX-Zoo: A PyTorch Toolbox for Face Recognition

2 code implementations12 Jan 2021 Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei

For example, the production of face representation network desires a modular training scheme to consider the proper choice from various candidates of state-of-the-art backbone and training supervision subject to the real-world face recognition demand; for performance analysis and comparison, the standard and automatic evaluation with a bunch of models on multiple benchmarks will be a desired tool as well; besides, a public groundwork is welcomed for deploying the face recognition in the shape of holistic pipeline.

Face Recognition

Reinforcement Learning for Control with Probabilistic Stability Guarantee

no code implementations1 Jan 2021 Minghao Han, Zhipeng Zhou, Lixian Zhang, Jun Wang, Wei Pan

Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable.

Multi-Agent Trust Region Learning

1 code implementation1 Jan 2021 Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.

Atari Games Multi-agent Reinforcement Learning +1

Understanding and Leveraging Causal Relations in Deep Reinforcement Learning

no code implementations1 Jan 2021 Peng Zhang, Furui Liu, Zhitang Chen, Jianye Hao, Jun Wang

Reinforcement Learning (RL) has shown great potential to deal with sequential decision-making problems.

Decision Making

Learning Predictive Communication by Imagination in Networked System Control

no code implementations1 Jan 2021 Yali Du, Yifan Zhao, Meng Fang, Jun Wang, Gangyan Xu, Haifeng Zhang

Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.

Learning to Explore with Pleasure

no code implementations1 Jan 2021 Yean Hoon Ong, Jun Wang

Exploration is a long-standing challenge in sequential decision problem in machine learning.

Bayesian Optimisation

TextTN: Probabilistic Encoding of Language on Tensor Network

no code implementations1 Jan 2021 Peng Zhang, Jing Zhang, Xindian Ma, Siwei Rao, Guangjian Tian, Jun Wang

As a novel model that bridges machine learning and quantum theory, tensor network (TN) has recently gained increasing attention and successful applications for processing natural images.

General Classification Sentiment Analysis +1

Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium

no code implementations1 Jan 2021 Yizheng Hu, Kun Shao, Dong Li, Jianye Hao, Wulong Liu, Yaodong Yang, Jun Wang, Zhanxing Zhu

Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution.

Adversarial Robustness SMAC

Regioned Episodic Reinforcement Learning

no code implementations1 Jan 2021 Jiarui Jin, Cong Chen, Ming Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Yong Yu, Jun Wang, Alex Smola

Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.

MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking

1 code implementation ICCV 2021 Zhoutao Wang, Qian Xie, Yu-Kun Lai, Jing Wu, Kun Long, Jun Wang

To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods.

3D Object Detection Visual Tracking

VENet: Voting Enhancement Network for 3D Object Detection

no code implementations ICCV 2021 Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Dening Lu, Mingqiang Wei, Jun Wang

Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.

3D Object Detection

Adaptive Curriculum Learning

no code implementations ICCV 2021 Yajing Kong, Liu Liu, Jun Wang, DaCheng Tao

Therefore, in contrast to recent works using a fixed curriculum, we devise a new curriculum learning method, Adaptive Curriculum Learning (Adaptive CL), adapting the difficulty of examples to the current state of the model.

Causal World Models by Unsupervised Deconfounding of Physical Dynamics

no code implementations28 Dec 2020 Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang

The capability of imagining internally with a mental model of the world is vitally important for human cognition.

Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction

no code implementations22 Dec 2020 Yao Zhang, Xu Zhang, Jun Wang, Hongru Liang, Wenqiang Lei, Zhe Sun, Adam Jatowt, Zhenglu Yang

The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.

Knowledge Graphs Link Prediction

Learn molecular representations from large-scale unlabeled molecules for drug discovery

no code implementations21 Dec 2020 Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, Sen Song

Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.

Drug Discovery

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

2 code implementations18 Dec 2020 Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos santos, Bing Xiang

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).

Language Modelling Self-Supervised Learning +2

Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions

no code implementations9 Dec 2020 Jun Wang, Shaoguo Wen, Kaixing Chen, Jianghua Yu, Xin Zhou, Peng Gao, Changsheng Li, Guotong Xie

Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection.

Active Learning Image Classification +3

A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface

no code implementations3 Dec 2020 Yingzhuo Sun, Cheng-Xiang Wang, Jie Huang, Jun Wang

The evolution of clusters on the linear array and planar array is also considered in the proposed model.

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets

no code implementations NeurIPS 2020 Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.

Measuring Correlation-to-Causation Exaggeration in Press Releases

1 code implementation COLING 2020 Bei Yu, Jun Wang, Lu Guo, Yingya Li

By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22{\%} of press releases made exaggerated causal claims from correlational findings in observational studies.

Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network

no code implementations29 Nov 2020 Haotian Xie, Yong Zhang, Jun Wang, Jingjing Zhang, Yifan Ma, Zhaogang Yang

The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer.

Data Augmentation Image Reconstruction

Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee

no code implementations13 Nov 2020 Minghao Han, Yuan Tian, Lixian Zhang, Jun Wang, Wei Pan

In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety.

Continuous Control

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images

1 code implementation11 Nov 2020 Jingxiong Li, Yaqi Wang, Shuai Wang, Jun Wang, Jun Liu, Qun Jin, Lingling Sun

Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.

COVID-19 Diagnosis General Classification

A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning

no code implementations2 Nov 2020 Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzman, Benjamin I. P. Rubinstein, Trevor Cohn

In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data.

Data Poisoning Machine Translation +1

Diversify Question Generation with Continuous Content Selectors and Question Type Modeling

no code implementations Findings of the Association for Computational Linguistics 2020 Zhen Wang, Siwei Rao, Jie Zhang, Zhen Qin, Guangjian Tian, Jun Wang

However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression.

Question Generation

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

no code implementations1 Nov 2020 Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu

To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.

Click-Through Rate Prediction Learning-To-Rank +1

An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective

1 code implementation1 Nov 2020 Yaodong Yang, Jun Wang

In this work, we provide a monograph on MARL that covers both the fundamentals and the latest developments in the research frontier.

Multi-agent Reinforcement Learning

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

3 code implementations ICLR 2021 Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney

Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization.

Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network

no code implementations10 Oct 2020 Jun Wang, Qianying Liu, Haotian Xie, Zhaogang Yang, Hefeng Zhou

In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer.

Data Augmentation Image Cropping +1

Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding

no code implementations9 Oct 2020 Jin Cao, Jun Wang, Wael Hamza, Kelly Vanee, Shang-Wen Li

The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency.

Domain Adaptation Language Modelling +1

Multi-typed Objects Multi-view Multi-instance Multi-label Learning

no code implementations6 Oct 2020 Yuanlin Yang, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.

Multi-Label Learning

Deep Incomplete Multi-View Multiple Clusterings

no code implementations2 Oct 2020 Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.

A Real-time Contribution Measurement Method for Participants in Federated Learning

no code implementations28 Sep 2020 Bingjie Yan, Yize Zhou, Boyi Liu, Jun Wang, Yuhan Zhang, Li Liu, Xiaolan Nie, Zhiwei Fan, Zhixuan Liang

However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.

Federated Learning

Multi-modal Summarization for Video-containing Documents

1 code implementation17 Sep 2020 Xiyan Fu, Jun Wang, Zhenglu Yang

Summarization of multimedia data becomes increasingly significant as it is the basis for many real-world applications, such as question answering, Web search, and so forth.

Question Answering Video Summarization

3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation

no code implementations15 Sep 2020 Yuanpeng Liu, Jun Zhou, Yuqi Zhang, Chao Ding, Jun Wang

To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses.

6D Pose Estimation

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.

Liouville type theorems and periodic solutions for $χ^{(2)}$ type systems with non-homogeneous nonlinearities

no code implementations30 Aug 2020 Aleks Jevnikar, Jun Wang, Wen Yang

In the present paper we derive Liouville type results and existence of periodic solutions for $\chi^{(2)}$ type systems with non-homogeneous nonlinearities.

Analysis of PDEs

Time irreversibility and amplitude irreversibility measures for nonequilibrium processes

no code implementations19 Aug 2020 Wenpo Yao, Jun Wang, Matjaz Perc, Wenli Yao, Jiafei Dai, Daqing Guo, Dezhong Yao

Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility -- a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations.

Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach

no code implementations15 Aug 2020 Lei LI, Qihang Peng, Jun Wang

The first is based on multi-view convolutional neural network by treating signals from different receive antennas as different views of a 3D object and designing the location and operation of view-pooling layer that are suitable for feature fusion of multi-antenna signals.

Bilevel Learning Model Towards Industrial Scheduling

no code implementations10 Aug 2020 Longkang Li, Hui-Ling Zhen, Mingxuan Yuan, Jiawen Lu, XialiangTong, Jia Zeng, Jun Wang, Dirk Schnieders

In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.

NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition

no code implementations20 Jul 2020 Dan Zeng, Hailin Shi, Hang Du, Jun Wang, Zhen Lei, Tao Mei

However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.

Face Recognition

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

1 code implementation ECCV 2020 Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.

Image Generation Neural Architecture Search

InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

no code implementations ECCV 2020 Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis

Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9. 0% mAP on the nuScenes test set.

3D Object Detection Autonomous Driving +1

Semi-Siamese Training for Shallow Face Learning

2 code implementations ECCV 2020 Hang Du, Hailin Shi, Yuchi Liu, Jun Wang, Zhen Lei, Dan Zeng, Tao Mei

Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.

Face Recognition

Bidirectional Loss Function for Label Enhancement and Distribution Learning

no code implementations7 Jul 2020 Xinyuan Liu, Jihua Zhu, Qinghai Zheng, Zhongyu Li, Ruixin Liu, Jun Wang

More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.

Multi-Label Learning

Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling

no code implementations18 Jun 2020 Meng Ding, Xiao Fu, Ting-Zhu Huang, Jun Wang, Xi-Le Zhao

This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.

Super-Resolution Tensor Decomposition

SAMBA: Safe Model-Based & Active Reinforcement Learning

no code implementations12 Jun 2020 Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Abdullah, Aivar Sootla, Jun Wang, Haitham Ammar

In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.

Safe Reinforcement Learning

Learning to Model Opponent Learning

1 code implementation6 Jun 2020 Ian Davies, Zheng Tian, Jun Wang

In this work, we develop a novel approach to modelling an opponent's learning dynamics which we term Learning to Model Opponent Learning (LeMOL).

Decision Making Multi-agent Reinforcement Learning

Multi-Agent Determinantal Q-Learning

1 code implementation ICML 2020 Yaodong Yang, Ying Wen, Li-Heng Chen, Jun Wang, Kun Shao, David Mguni, Wei-Nan Zhang

Though practical, current methods rely on restrictive assumptions to decompose the centralized value function across agents for execution.

Q-Learning

A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices

no code implementations26 May 2020 Cong Wang, Yanru Xiao, Xing Gao, Li Li, Jun Wang

We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer.

Metric Learning Multi-class Classification

Learning from a Lightweight Teacher for Efficient Knowledge Distillation

no code implementations19 May 2020 Yuang Liu, Wei zhang, Jun Wang

Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the soft target generated by well-trained teachers.

Knowledge Distillation

ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language

2 code implementations ECCV 2020 Zhe Wang, Zhiyuan Fang, Jun Wang, Yezhou Yang

Person search by natural language aims at retrieving a specific person in a large-scale image pool that matches the given textual descriptions.

Contrastive Learning Person Search +1

Adaptive Structural Fingerprints for Graph Attention Networks

no code implementations ICLR 2020 Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang

Yet, how to fully exploit rich structural information in the attention mechanism remains a challenge.

Graph Attention

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 Apr 2020 Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.

Information Retrieval Survival Analysis

Actor-Critic Reinforcement Learning for Control with Stability Guarantee

no code implementations29 Apr 2020 Minghao Han, Lixian Zhang, Jun Wang, Wei Pan

Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.

Motion Planning

Deep Feature-preserving Normal Estimation for Point Cloud Filtering

no code implementations24 Apr 2020 Dening Lu, Xuequan Lu, Yangxing Sun, Jun Wang

In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

no code implementations23 Apr 2020 Jingwei Song, Shaobo Xia, Jun Wang, Mitesh Patel, Dong Chen

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.

Hyperspectral Image Denoising Image Denoising

CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models

1 code implementation CVPR 2021 Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.

Representation Learning

ControlVAE: Controllable Variational Autoencoder

no code implementations ICML 2020 Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher

Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning.

Image Generation Language Modelling +1

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1 code implementation6 Apr 2020 Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen

In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

Computed Tomography (CT)

Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives

no code implementations22 Mar 2020 Jingwei Song, Shaobo Xia, Jun Wang, Dong Chen

To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.