Search Results for author: Siheng Chen

Found 52 papers, 17 papers with code

Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning

no code implementations27 Jun 2022 Chenxin Xu, Yuxi Wei, Bohan Tang, Sheng Yin, Ya zhang, Siheng Chen

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction.

Relational Reasoning Trajectory Prediction

Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling

no code implementations31 May 2022 Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen, Junni Zou, Pascal Frossard, Hongkai Xiong

Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).

GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning

1 code implementation CVPR 2022 Chenxin Xu, Maosen Li, Zhenyang Ni, Ya zhang, Siheng Chen

From the aspect of interaction capturing, we propose a trainable multiscale hypergraph to capture both pair-wise and group-wise interactions at multiple group sizes.

Relational Reasoning Representation Learning +1

Remember Intentions: Retrospective-Memory-based Trajectory Prediction

1 code implementation CVPR 2022 Chenxin Xu, Weibo Mao, Wenjun Zhang, Siheng Chen

However, in this way, the model parameters come from all seen instances, which means a huge amount of irrelevant seen instances might also involve in predicting the current situation, disturbing the performance.

Trajectory Prediction

V2X-Sim: A Virtual Collaborative Perception Dataset for Autonomous Driving

no code implementations17 Feb 2022 Yiming Li, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, Chen Feng

Vehicle-to-everything (V2X), which denotes the collaboration between a vehicle and any entity in its surrounding, can fundamentally improve the perception in self-driving systems.

Autonomous Driving

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

no code implementations17 Feb 2022 Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

Multivariate Time Series Forecasting Time Series +1

Task Decoupled Framework for Reference-Based Super-Resolution

no code implementations CVPR 2022 Yixuan Huang, Xiaoyun Zhang, Yu Fu, Siheng Chen, Ya zhang, Yan-Feng Wang, Dazhi He

Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.

Image Super-Resolution Reference-based Super-Resolution +1

No-Reference Point Cloud Quality Assessment via Domain Adaptation

1 code implementation CVPR 2022 Qi Yang, Yipeng Liu, Siheng Chen, Yiling Xu, Jun Sun

We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds.

Domain Adaptation Point Cloud Quality Assessment

Learning Distilled Collaboration Graph for Multi-Agent Perception

1 code implementation NeurIPS 2021 Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, Wenjun Zhang

Our approach is validated on V2X-Sim 1. 0, a large-scale multi-agent perception dataset that we synthesized using CARLA and SUMO co-simulation.

3D Object Detection Knowledge Distillation +1

Dynamic Differential-Privacy Preserving SGD

no code implementations30 Oct 2021 Jian Du, Song Li, Xiangyi Chen, Siheng Chen, Mingyi Hong

The equivalent privacy costs controlled by maintaining the same gradient clipping thresholds and noise powers in each step result in unstable updates and a lower model accuracy when compared to the non-DP counterpart.

Federated Learning Image Classification +2

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

no code implementations NeurIPS 2021 Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya zhang, Siheng Chen

2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.

Trajectory Forecasting

Spatio-Temporal Graph Complementary Scattering Networks

no code implementations23 Oct 2021 Zida Cheng, Siheng Chen, Ya zhang

Spatio-temporal graph signal analysis has a significant impact on a wide range of applications, including hand/body pose action recognition.

Action Recognition

Learning to Learn Graph Topologies

1 code implementation NeurIPS 2021 Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen

The model is trained in an end-to-end fashion with pairs of node data and graph samples.

Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph

1 code implementation16 Oct 2021 Xin Yu, Jeroen van Baar, Siheng Chen

We use a coarse graph, derived from a dense graph, to estimate the human's 3D pose, and the dense graph to estimate the 3D shape.

3D Human Pose Estimation

A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer

no code implementations24 Sep 2021 Jinxiang Liu, Yangheng Zhao, Siheng Chen, Ya zhang

To leverage the human body shape prior, LPNet exploits the topological information of the body mesh to learn an expressive visual representation for the target person in the 3D mesh space.

Image-to-Image Translation Pose Transfer +1

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction

no code implementations25 Aug 2021 Maosen Li, Siheng Chen, Yangheng Zhao, Ya zhang, Yanfeng Wang, Qi Tian

The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales.

motion prediction

CaT: Weakly Supervised Object Detection with Category Transfer

no code implementations ICCV 2021 Tianyue Cao, Lianyu Du, Xiaoyun Zhang, Siheng Chen, Ya zhang, Yan-Feng Wang

To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets.

object-detection Transfer Learning +1

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network

no code implementations2 Jul 2021 Maosen Li, Siheng Chen, Yanning Shen, Genjia Liu, Ivor W. Tsang, Ya zhang

This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system.

Human motion prediction motion prediction

Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization

no code implementations6 Apr 2021 Chen Ju, Peisen Zhao, Siheng Chen, Ya zhang, Xiaoyun Zhang, Qi Tian

To solve this issue, we introduce an adaptive mutual supervision framework (AMS) with two branches, where the base branch adopts CAS to localize the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through a novel adaptive sampler.

Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1

MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy

1 code implementation4 Mar 2021 Qi Yang, Yujie Zhang, Siheng Chen, Yiling Xu, Jun Sun, Zhan Ma

In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).

Point cloud reconstruction

Divide and Conquer for Single-Frame Temporal Action Localization

no code implementations ICCV 2021 Chen Ju, Peisen Zhao, Siheng Chen, Ya zhang, Yanfeng Wang, Qi Tian

Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.

Temporal Action Localization

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

1 code implementation17 Dec 2020 Chenxin Xu, Siheng Chen, Maosen Li, Ya zhang

To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network.

3D Human Pose Estimation Knowledge Distillation +1

Spatio-Temporal Graph Scattering Transform

no code implementations ICLR 2021 Chao Pan, Siheng Chen, Antonio Ortega

Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.

Time Series

Sampling and Recovery of Graph Signals based on Graph Neural Networks

no code implementations3 Nov 2020 Siheng Chen, Maosen Li, Ya zhang

Compared to previous analytical sampling and recovery, the proposed methods are able to flexibly learn a variety of graph signal models from data by leveraging the learning ability of neural networks; compared to previous neural-network-based sampling and recovery, the proposed methods are designed through exploiting specific graph properties and provide interpretability.

Graph Classification

Learning on Attribute-Missing Graphs

2 code implementations3 Nov 2020 Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya zhang, Ivor W Tsang

Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.

Graph Learning Link Prediction

Graph Cross Networks with Vertex Infomax Pooling

2 code implementations NeurIPS 2020 Maosen Li, Siheng Chen, Ya zhang, Ivor W. Tsang

Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow.

Classification General Classification +1

Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

no code implementations16 Jul 2020 Jingchao Su, Xu Chen, Ya zhang, Siheng Chen, Dan Lv, Chenyang Li

The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs.

Relational Reasoning Transfer Learning

Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks

no code implementations17 Jun 2020 Takuya Fujihashi, Toshiaki Koike-Akino, Siheng Chen, Takashi Watanabe

To prevent the cliff effect subject to channel quality fluctuation, we have proposed soft point cloud delivery called HoloCast.

3D Reconstruction Video Compression

Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising

no code implementations1 Jun 2020 Siheng Chen, Yonina C. Eldar, Lingxiao Zhao

We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals.

Denoising

Pruned Graph Scattering Transforms

no code implementations ICLR 2020 Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks.

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

1 code implementation17 Mar 2020 Maosen Li, Siheng Chen, Yangheng Zhao, Ya zhang, Yan-Feng Wang, Qi Tian

The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.

3D Human Pose Estimation 3D Pose Estimation +2

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

1 code implementation CVPR 2020 Pengxiang Wu, Siheng Chen, Dimitris Metaxas

The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.

3D Object Detection Autonomous Driving +2

Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring

no code implementations6 Feb 2020 Jingxiao Liu, Bingqing Chen, Siheng Chen, Mario Berges, Jacobo Bielak, HaeYoung Noh

We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.

Efficient and Stable Graph Scattering Transforms via Pruning

no code implementations27 Jan 2020 Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs.

3D Point Cloud Classification Graph Learning +1

Node Attribute Generation on Graphs

2 code implementations23 Jul 2019 Xu Chen, Siheng Chen, Huangjie Zheng, Jiangchao Yao, Kenan Cui, Ya zhang, Ivor W. Tsang

NANG learns a unifying latent representation which is shared by both node attributes and graph structures and can be translated to different modalities.

Data Augmentation General Classification +2

Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving

no code implementations26 Jun 2019 Siheng Chen, Sufeng. Niu, Tian Lan, Baoan Liu

We present a novel graph-neural-network-based system to effectively represent large-scale 3D point clouds with the applications to autonomous driving.

Autonomous Driving Self-Driving Cars

Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

no code implementations11 May 2019 Siheng Chen, Chaojing Duan, Yaoqing Yang, Duanshun Li, Chen Feng, Dong Tian

The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; and (3) graph filtering refines the reconstruction, leading to better performances.

3D Point Cloud Reconstruction General Classification +1

Handwritten Chinese Font Generation with Collaborative Stroke Refinement

no code implementations30 Apr 2019 Chuan Wen, Jie Chang, Ya zhang, Siheng Chen, Yan-Feng Wang, Mei Han, Qi Tian

Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters.

Font Generation

3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation

1 code implementation9 Apr 2019 Chaojing Duan, Siheng Chen, Jelena Kovacevic

NPD algorithm uses a neural network to estimate reference planes for points in noisy point clouds.

Denoising

Generalized Value Iteration Networks: Life Beyond Lattices

1 code implementation8 Jun 2017 Sufeng. Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa C. Smith, Jelena Kovačević

GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs.

Q-Learning

Fast Resampling of 3D Point Clouds via Graphs

no code implementations11 Feb 2017 Siheng Chen, Dong Tian, Chen Feng, Anthony Vetro, Jelena Kovačević

We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.

Signal Representations on Graphs: Tools and Applications

no code implementations16 Dec 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.

A statistical perspective of sampling scores for linear regression

no code implementations21 Jul 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

In this paper, we consider a statistical problem of learning a linear model from noisy samples.

Signal Recovery on Graphs: Random versus Experimentally Designed Sampling

no code implementations21 Apr 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.

Signal Recovery on Graphs: Variation Minimization

no code implementations26 Nov 2014 Siheng Chen, Aliaksei Sandryhaila, José M. F. Moura, Jelena Kovačević

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.

Anomaly Detection General Classification +2

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