Search Results for author: Ye Yuan

Found 110 papers, 43 papers with code

FIMO: A Challenge Formal Dataset for Automated Theorem Proving

no code implementations8 Sep 2023 Chengwu Liu, Jianhao Shen, Huajian Xin, Zhengying Liu, Ye Yuan, Haiming Wang, Wei Ju, Chuanyang Zheng, Yichun Yin, Lin Li, Ming Zhang, Qun Liu

We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.

Automated Theorem Proving

Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition

no code implementations21 Aug 2023 Zhuang Liu, Ye Yuan, Zhilong Ji, Jingfeng Bai, Xiang Bai

Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space.

Graph Representation Learning

TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

1 code implementation20 Jul 2023 Wendi Li, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Ye Yuan, Wenfeng Xie, Dangyang Chen

TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results.

Knowledge Graphs Recommendation Systems

DREAM: Domain-free Reverse Engineering Attributes of Black-box Model

no code implementations20 Jul 2023 Rongqing Li, Jiaqi Yu, Changsheng Li, Wenhan Luo, Ye Yuan, Guoren Wang

There is a crucial limitation: these works assume the dataset used for training the target model to be known beforehand and leverage this dataset for model attribute attack.

Shared Growth of Graph Neural Networks via Free-direction Knowledge Distillation

no code implementations2 Jul 2023 Kaituo Feng, Yikun Miao, Changsheng Li, Ye Yuan, Guoren Wang

Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN.

Knowledge Distillation Transfer Learning

NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations

1 code implementation10 Jun 2023 Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan Lin

Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes.

Adversarial Robustness Novel View Synthesis

Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning

no code implementations24 Apr 2023 Yonggan Fu, Ye Yuan, Shang Wu, Jiayi Yuan, Yingyan Lin

Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks.

Adversarial Robustness Transfer Learning

Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion

no code implementations CVPR 2023 Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, Or Litany

We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.

Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption

no code implementations15 Feb 2023 Jun Liu, Ye Yuan

We prove that various stochastic gradient descent methods, including the stochastic gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's accelerated gradient (SNAG) methods, almost surely avoid any strict saddle manifold.

Learning Human Dynamics in Autonomous Driving Scenarios

no code implementations ICCV 2023 Jingbo Wang, Ye Yuan, Zhengyi Luo, Kevin Xie, Dahua Lin, Umar Iqbal, Sanja Fidler, Sameh Khamis

In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.

Autonomous Driving Human Dynamics

PhysDiff: Physics-Guided Human Motion Diffusion Model

no code implementations ICCV 2023 Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, Jan Kautz

Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion.


A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

no code implementations30 Nov 2022 Ying Wang, Ye Yuan, Xin Luo

Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG.

Model Optimization Representation Learning

Prototype as Query for Few Shot Semantic Segmentation

1 code implementation27 Nov 2022 Leilei Cao, Yibo Guo, Ye Yuan, Qiangguo Jin

In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.

Few-Shot Semantic Segmentation

1st Place Solutions for UG2+ Challenge 2022 ATMOSPHERIC TURBULENCE MITIGATION

no code implementations30 Oct 2022 Zhuang Liu, Zhichao Zhao, Ye Yuan, Zhi Qiao, Jinfeng Bai, Zhilong Ji

In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG$^2$+ Challenge in CVPR 2022.

Image Quality Assessment Image Reconstruction

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective

no code implementations NeurIPS 2020 Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang

What's more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes.

Pedestrian Detection

Global Prototype Encoding for Incremental Video Highlights Detection

no code implementations12 Sep 2022 Sen Pei, Shixiong Xu, Ye Yuan, Jiashi Feng, Xiaohui Shen, Xiaojie Jin

To the best of our knowledge, this is the first time the incremental learning settings are introduced to video highlights detection, which in turn relieves the burden of training video inputs and promotes the scalability of conventional neural networks in proportion to both the size of the dataset and the quantity of domains.

Incremental Learning

A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model

no code implementations4 Aug 2022 Jinli Li, Ye Yuan

High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications.

Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization

no code implementations4 Aug 2022 Jiufang Chen, Ye Yuan

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix.

When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition

1 code implementation23 Jul 2022 Bohan Li, Ye Yuan, Dingkang Liang, Xiao Liu, Zhilong Ji, Jinfeng Bai, Wenyu Liu, Xiang Bai

Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images with the attention mechanism.

Optical Character Recognition (OCR)

Robust Knowledge Adaptation for Dynamic Graph Neural Networks

no code implementations22 Jul 2022 Hanjie Li, Changsheng Li, Kaituo Feng, Ye Yuan, Guoren Wang, Hongyuan Zha

Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling such graph data, where almost all the existing approaches assume that when a new link is built, the embeddings of the neighbor nodes should be updated by learning the temporal dynamics to propagate new information.

reinforcement-learning Reinforcement Learning (RL)

Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration

1 code implementation28 Jun 2022 Yanjiang Yu, Puyang Zhang, Kaihao Zhang, Wenhan Luo, Changsheng Li, Ye Yuan, Guoren Wang

To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.

Blind Face Restoration Neural Architecture Search

SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration

no code implementations27 Jun 2022 Xiao Fan, Shuxin Zhuang, Zhemin Zhuang, Ye Yuan, Shunmin Qiu, Alex Noel Joseph Raj, Yibiao Rong

Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis.

Image Registration Motion Estimation

From Universal Humanoid Control to Automatic Physically Valid Character Creation

no code implementations18 Jun 2022 Zhengyi Luo, Ye Yuan, Kris M. Kitani

Second, we use a design-and-control framework to optimize a humanoid's physical attributes to find body designs that can better imitate the pre-specified human motion sequence(s).

Humanoid Control

Embodied Scene-aware Human Pose Estimation

no code implementations18 Jun 2022 Zhengyi Luo, Shun Iwase, Ye Yuan, Kris Kitani

Since 2D third-person observations are coupled with the camera pose, we propose to disentangle the camera pose and use a multi-step projection gradient defined in the global coordinate frame as the movement cue for our embodied agent.

3D Human Pose Estimation Causal Inference +1

FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks

no code implementations14 Jun 2022 Kaituo Feng, Changsheng Li, Ye Yuan, Guoren Wang

Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN.

Knowledge Distillation reinforcement-learning +2

Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

1 code implementation3 Jun 2022 Yanglan Ou, Ye Yuan, Xiaolei Huang, Stephen T. C. Wong, John Volpi, James Z. Wang, Kelvin Wong

We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel.

Image Segmentation Lesion Segmentation +1

Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression

no code implementations24 May 2022 Jiachen Li, Ye Yuan, Hong-Bin Shen

Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data.

regression Symbolic Regression

PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis

no code implementations5 May 2022 Ye Yuan, Xin Luo

A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes.

Representation Learning

A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systems

no code implementations29 Apr 2022 Zhexuan Zeng, Zuogong Yue, Alexandre Mauroy, Jorge Goncalves, Ye Yuan

The necessary and sufficient condition is proposed -- which is built from Koopman operator -- to the exact identification of the CT system from sampled data.

Unified Simulation, Perception, and Generation of Human Behavior

no code implementations28 Apr 2022 Ye Yuan

Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans.

Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering

no code implementations26 Apr 2022 Shiye Wang, Changsheng Li, Yanming Li, Ye Yuan, Guoren Wang

Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.

Clustering Multi-view Subspace Clustering

Online No-regret Model-Based Meta RL for Personalized Navigation

no code implementations5 Apr 2022 Yuda Song, Ye Yuan, Wen Sun, Kris Kitani

Our theoretical analysis shows that our method is a no-regret algorithm and we provide the convergence rate in the agnostic setting.

Model-based Reinforcement Learning

Adaptive Divergence-based Non-negative Latent Factor Analysis

no code implementations30 Mar 2022 Ye Yuan, Guangxiao Yuan, Renfang Wang, Xin Luo

High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions.

Syntax-Aware Network for Handwritten Mathematical Expression Recognition

2 code implementations CVPR 2022 Ye Yuan, Xiao Liu, Wondimu Dikubab, Hui Liu, Zhilong Ji, Zhongqin Wu, Xiang Bai

In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network.

Molecule Generation for Drug Design: a Graph Learning Perspective

no code implementations18 Feb 2022 Nianzu Yang, Huaijin Wu, Junchi Yan, Xiaoyong Pan, Ye Yuan, Le Song

From the application perspective, one of the emerging and attractive areas is aiding the design and discovery of molecules, especially in drug industry.

Graph Learning

On Almost Sure Convergence Rates of Stochastic Gradient Methods

no code implementations9 Feb 2022 Jun Liu, Ye Yuan

We further provide last-iterate almost sure convergence rates analysis for stochastic gradient methods on weakly convex smooth functions, in contrast with most existing results in the literature that only provide convergence in expectation for a weighted average of the iterates.

Boosting Contrastive Learning with Relation Knowledge Distillation

no code implementations8 Dec 2021 Kai Zheng, Yuanjiang Wang, Ye Yuan

We delve into this problem and find that the lightweight model is prone to collapse in semantic space when simply performing instance-wise contrast.

Contrastive Learning Knowledge Distillation +1

GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras

1 code implementation CVPR 2022 Ye Yuan, Umar Iqbal, Pavlo Molchanov, Kris Kitani, Jan Kautz

Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.

Global 3D Human Pose Estimation Human Mesh Recovery

Deep Unsupervised Active Learning on Learnable Graphs

no code implementations8 Nov 2021 Handong Ma, Changsheng Li, Xinchu Shi, Ye Yuan, Guoren Wang

To make the learnt graph structure more stable and effective, we take into account $k$-nearest neighbor graph as a priori, and learn a relation propagation graph structure.

Active Learning Graph structure learning +1

FBNet: Feature Balance Network for Urban-Scene Segmentation

no code implementations5 Nov 2021 Lei Gan, Huabin Huang, Banghuai Li, Ye Yuan

In this paper, we present a novel add-on module, named Feature Balance Network (FBNet), to eliminate the feature camouflage in urban-scene segmentation.

Autonomous Driving Image Segmentation +1

Causal Effect Estimation using Variational Information Bottleneck

1 code implementation26 Oct 2021 Zhenyu Lu, Yurong Cheng, Mingjun Zhong, George Stoian, Ye Yuan, Guoren Wang

A typical approach is to formulate causal inference as a supervised learning problem and so counterfactual could be predicted.

Causal Inference

Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design

1 code implementation ICLR 2022 Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani

Specifically, we learn a conditional policy that, in an episode, first applies a sequence of transform actions to modify an agent's skeletal structure and joint attributes, and then applies control actions under the new design.

Decision Making Policy Gradient Methods

Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis

1 code implementation22 Sep 2021 Zeyuan Yin, Ye Yuan, Panfeng Guo, Pan Zhou

Edge devices in federated learning usually have much more limited computation and communication resources compared to servers in a data center.

Backdoor Attack Federated Learning +1

Font Completion and Manipulation by Cycling Between Multi-Modality Representations

1 code implementation30 Aug 2021 Ye Yuan, Wuyang Chen, Zhaowen Wang, Matthew Fisher, Zhifei Zhang, Zhangyang Wang, Hailin Jin

The novel graph constructor maps a glyph's latent code to its graph representation that matches expert knowledge, which is trained to help the translation task.

Image-to-Image Translation Representation Learning +2

Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study

1 code implementation23 Jul 2021 Zhenyu Wu, Zhaowen Wang, Ye Yuan, Jianming Zhang, Zhangyang Wang, Hailin Jin

Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale, and/or depends on the access to original training data as well as the trained model parameters.

Image Generation

DeceFL: A Principled Decentralized Federated Learning Framework

1 code implementation15 Jul 2021 Ye Yuan, Jun Liu, Dou Jin, Zuogong Yue, Ruijuan Chen, Maolin Wang, Chuan Sun, Lei Xu, Feng Hua, Xin He, Xinlei Yi, Tao Yang, Hai-Tao Zhang, Shaochun Sui, Han Ding

Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.

Federated Learning Privacy Preserving

Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation

1 code implementation NeurIPS 2021 Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Kris Kitani

By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model.

Egocentric Pose Estimation Human-Object Interaction Detection +1

SimPoE: Simulated Character Control for 3D Human Pose Estimation

no code implementations CVPR 2021 Ye Yuan, Shih-En Wei, Tomas Simon, Kris Kitani, Jason Saragih

Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e. g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame.

3D Human Pose Estimation

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

3 code implementations CVPR 2021 Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, Chi Zhang

We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.

Contrastive Learning Few-Shot Learning +3

Causal inference using deep neural networks

no code implementations25 Nov 2020 Ye Yuan, Xueying Ding, Ziv Bar-Joseph

Causal inference from observation data is a core problem in many scientific fields.

Causal Inference

Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation

no code implementations10 Nov 2020 Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Shun Iwase, Kris M. Kitani

We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation using a head-mounted camera.

Human-Object Interaction Detection Pose Estimation +2

Scalable Graph Neural Networks via Bidirectional Propagation

1 code implementation NeurIPS 2020 Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen

Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.

Graph Sampling

PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning

no code implementations6 Oct 2020 Yuli Zheng, Zhenyu Wu, Ye Yuan, Tianlong Chen, Zhangyang Wang

While machine learning is increasingly used in this field, the resulting large-scale collection of user private information has reinvigorated the privacy debate, considering dozens of data breach incidents every year caused by unauthorized hackers, and (potentially even more) information misuse/abuse by authorized parties.

BIG-bench Machine Learning Privacy Preserving

End-to-End 3D Multi-Object Tracking and Trajectory Forecasting

no code implementations25 Aug 2020 Xinshuo Weng, Ye Yuan, Kris Kitani

To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units.

3D Multi-Object Tracking Trajectory Forecasting

Efficient Non-Line-of-Sight Imaging from Transient Sinograms

no code implementations ECCV 2020 Mariko Isogawa, Dorian Chan, Ye Yuan, Kris Kitani, Matthew O'Toole

Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e. g., walls) to see around corners.

On Deep Unsupervised Active Learning

no code implementations28 Jul 2020 Changsheng Li, Handong Ma, Zhao Kang, Ye Yuan, Xiao-Yu Zhang, Guoren Wang

Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.

Active Learning

AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses

1 code implementation7 Jul 2020 Zixuan Xu, Banghuai Li, Miao Geng, Ye Yuan

Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses.

Face Alignment Facial Landmark Detection

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

1 code implementation ICML 2020 Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples.

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis

1 code implementation NeurIPS 2020 Ye Yuan, Kris Kitani

Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3. 6M) and generates diverse long-term motions.

Humanoid Control Motion Synthesis

Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network

no code implementations1 Apr 2020 Yanglan Ou, Yuan Xue, Ye Yuan, Tao Xu, Vincent Pisztora, Jia Li, Xiaolei Huang

In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.

Classification General Classification

Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation

1 code implementation CVPR 2020 Mariko Isogawa, Ye Yuan, Matthew O'Toole, Kris Kitani

We bring together a diverse set of technologies from NLOS imaging, human pose estimation and deep reinforcement learning to construct an end-to-end data processing pipeline that converts a raw stream of photon measurements into a full 3D human pose sequence estimate.

3D Human Pose Estimation Humanoid Control

BoostTree and BoostForest for Ensemble Learning

1 code implementation21 Mar 2020 Changming Zhao, Dongrui Wu, Jian Huang, Ye Yuan, Hai-Tao Zhang, Ruimin Peng, Zhenhua Shi

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance.

Ensemble Learning General Classification +1

DLow: Diversifying Latent Flows for Diverse Human Motion Prediction

1 code implementation ECCV 2020 Ye Yuan, Kris Kitani

To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples.

 Ranked #1 on Human Pose Forecasting on AMASS (APD metric)

Human motion prediction Human Pose Forecasting +1

PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling

no code implementations17 Mar 2020 Xinshuo Weng, Ye Yuan, Kris Kitani

We evaluate on KITTI and nuScenes datasets showing that our method with socially-aware feature learning and diversity sampling achieves new state-of-the-art performance on 3D MOT and trajectory prediction.

3D Multi-Object Tracking Trajectory Forecasting

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

no code implementations3 Mar 2020 Zepeng Huo, Arash Pakbin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.

Clustering Human Activity Recognition

In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation

1 code implementation17 Dec 2019 Ye Yuan, Wuyang Chen, Yang Yang, Zhangyang Wang

This work addresses the above two shortcomings of triplet loss, extending its effectiveness to large-scale ReID datasets with potentially noisy labels.

Person Re-Identification

A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images

no code implementations13 Dec 2019 Ye Yuan, Jian Guan, Pengming Feng, Yanxia Wu

In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical solution for SAR despeckling (PSD) is proposed.

Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification

1 code implementation26 Nov 2019 Ye Yuan, Wuyang Chen, Tianlong Chen, Yang Yang, Zhou Ren, Zhangyang Wang, Gang Hua

Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification.

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines

4 code implementations14 Nov 2019 Yinda Xu, Zeyu Wang, Zuoxin Li, Ye Yuan, Gang Yu

Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4).

Ranked #2 on Visual Object Tracking on VOT2017/18 (using extra training data)

Classification General Classification +3

PowerSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization

no code implementations25 Sep 2019 Jun Liu, Beitong Zhou, Weigao Sun, Ruijuan Chen, Claire J. Tomlin, Ye Yuan

In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.

Machine Discovery of Partial Differential Equations from Spatiotemporal Data

1 code implementation15 Sep 2019 Ye Yuan, Junlin Li, Liang Li, Frank Jiang, Xiuchuan Tang, Fumin Zhang, Sheng Liu, Jorge Goncalves, Henning U. Voss, Xiuting Li, Jürgen Kurths, Han Ding

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data.

Diverse Trajectory Forecasting with Determinantal Point Processes

no code implementations ICLR 2020 Ye Yuan, Kris Kitani

To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).

Autonomous Vehicles Human Pose Forecasting +2

Ego-Pose Estimation and Forecasting as Real-Time PD Control

1 code implementation ICCV 2019 Ye Yuan, Kris Kitani

We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos.

Egocentric Pose Estimation Human Pose Forecasting +1

Generative Hybrid Representations for Activity Forecasting with No-Regret Learning

no code implementations CVPR 2020 Jiaqi Guan, Ye Yuan, Kris M. Kitani, Nicholas Rhinehart

Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.

UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments

no code implementations9 Apr 2019 Ye Yuan, Wenhan Yang, Wenqi Ren, Jiaying Liu, Walter J. Scheirer, Zhangyang Wang

The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.

Face Detection

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

no code implementations1 Apr 2019 Wenqian Jiang, Cheng Cheng, Beitong Zhou, Guijun Ma, Ye Yuan

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases.

Time Series Time Series Analysis

Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)

1 code implementation26 Mar 2019 Dongrui Wu, Ye Yuan, Yihua Tan

Our final algorithm, mini-batch gradient descent with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing.

Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis

no code implementations2 Mar 2019 Cheng Cheng, Beitong Zhou, Guijun Ma, Dongrui Wu, Ye Yuan

However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis.

Transfer Learning

A General End-to-end Diagnosis Framework for Manufacturing Systems

no code implementations17 Dec 2018 Ye Yuan, Guijun Ma, Cheng Cheng, Beitong Zhou, Huan Zhao, Hai-Tao Zhang, Han Ding

A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.


A deep learning-based remaining useful life prediction approach for bearings

1 code implementation8 Dec 2018 Cheng Cheng, Guijun Ma, Yong Zhang, Mingyang Sun, Fei Teng, Han Ding, Ye Yuan

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).

3D Ego-Pose Estimation via Imitation Learning

no code implementations ECCV 2018 Ye Yuan, Kris Kitani

Motivated by this, we propose a novel control-based approach to model human motion with physics simulation and use imitation learning to learn a video-conditioned control policy for ego-pose estimation.

Domain Adaptation Imitation Learning +1

SFace: An Efficient Network for Face Detection in Large Scale Variations

no code implementations18 Apr 2018 Jianfeng Wang, Ye Yuan, Boxun Li, Gang Yu, Sun Jian

A new dataset called 4K-Face is also introduced to evaluate the performance of face detection with extreme large scale variations.

Face Detection Face Recognition

Face Attention Network: An Effective Face Detector for the Occluded Faces

1 code implementation20 Nov 2017 Jianfeng Wang, Ye Yuan, Gang Yu

The performance of face detection has been largely improved with the development of convolutional neural network.

Data Augmentation Occluded Face Detection

On Identification of Distribution Grids

1 code implementation5 Nov 2017 Omid Ardakanian, Vincent W. S. Wong, Roel Dobbe, Steven H. Low, Alexandra von Meier, Claire Tomlin, Ye Yuan

Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis.

Understanding Convolution for Semantic Segmentation

5 code implementations27 Feb 2017 Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison Cottrell

This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.

Semantic Segmentation Thermal Image Segmentation

Joint Hand Detection and Rotation Estimation by Using CNN

no code implementations8 Dec 2016 Xiaoming Deng, Ye Yuan, Yinda Zhang, Ping Tan, Liang Chang, Shuo Yang, Hongan Wang

Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.

General Classification Hand Detection +2

Words or Characters? Fine-grained Gating for Reading Comprehension

1 code implementation6 Nov 2016 Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov

Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.

Question Answering Reading Comprehension +1

Inverse Power Flow Problem

no code implementations21 Oct 2016 Ye Yuan, Steven Low, Omid Ardakanian, Claire Tomlin

We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes).

On the Powerball Method for Optimization

no code implementations24 Mar 2016 Ye Yuan, Mu Li, Jun Liu, Claire J. Tomlin

We propose a new method to accelerate the convergence of optimization algorithms.

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