b'Ya Zhang'

research

∙ 06/27/2022

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

Demystifying the interactions among multiple agents from their past traj...

Chenxin Xu, et al. ∙

research

∙ 06/26/2022

Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation

We present a simple yet effective self-supervised framework for audio-vi...

Jinxiang Liu, et al. ∙

research

∙ 06/14/2022

K-Space Transformer for Fast MRI Reconstruction with Implicit Representation

This paper considers the problem of fast MRI reconstruction. We propose ...

Ziheng Zhao, et al. ∙

research

∙ 05/25/2022

Contrastive Learning with Boosted Memorization

Self-supervised learning has achieved a great success in the representat...

Zhihan Zhou, et al. ∙

research

∙ 05/13/2022

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Recently, anomaly detection and localization in multimedia data have rec...

Chaoqin Huang, et al. ∙

research

∙ 04/19/2022

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

Demystifying the interactions among multiple agents from their past traj...

Chenxin Xu, et al. ∙

research

∙ 12/08/2021

Prompting Visual-Language Models for Efficient Video Understanding

Visual-language pre-training has shown great success for learning joint ...

Chen Ju, et al. ∙

research

∙ 10/23/2021

Spatio-Temporal Graph Complementary Scattering Networks

Spatio-temporal graph signal analysis has a significant impact on a wide...

Zida Cheng, et al. ∙

research

∙ 09/27/2021

Click-through Rate Prediction with Auto-Quantized Contrastive Learning

Click-through rate (CTR) prediction becomes indispensable in ubiquitous ...

Yujie Pan, et al. ∙

research

∙ 09/24/2021

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

Human pose transfer has typically been modeled as a 2D image-to-image tr...

Jinxiang Liu, et al. ∙

research

∙ 09/07/2021

Self-supervised Tumor Segmentation through Layer Decomposition

In this paper, we propose a self-supervised approach for tumor segmentat...

Xiaoman Zhang, et al. ∙

research

∙ 08/25/2021

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

We propose a multiscale spatio-temporal graph neural network (MST-GNN) t...

Maosen Li, et al. ∙

research

∙ 08/17/2021

CaT: Weakly Supervised Object Detection with Category Transfer

A large gap exists between fully-supervised object detection and weakly-...

Tianyue Cao, et al. ∙

research

∙ 08/11/2021

Cooperative Learning for Noisy Supervision

Learning with noisy labels has gained the enormous interest in the robus...

Hao Wu, et al. ∙

research

∙ 08/05/2021

MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets

Annotating multiple organs in 3D medical images is time-consuming and co...

Shixiang Feng, et al. ∙

research

∙ 07/16/2021

Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning

3D hand-object pose estimation is an important issue to understand the i...

Zida Cheng, et al. ∙

research

∙ 07/02/2021

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network

This paper considers predicting future statuses of multiple agents in an...

Maosen Li, et al. ∙

research

∙ 06/17/2021

Knowledge distillation from multi-modal to mono-modal segmentation networks

The joint use of multiple imaging modalities for medical image segmentat...

Minhao Hu, et al. ∙

research

∙ 05/24/2021

A Fourier-based Framework for Domain Generalization

Modern deep neural networks suffer from performance degradation when eva...

Qinwei Xu, et al. ∙

research

∙ 04/06/2021

Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization aims to localize actions ...

Chen Ju, et al. ∙

research

∙ 03/31/2021

Collaborative Label Correction via Entropy Thresholding

Deep neural networks (DNNs) have the capacity to fit extremely noisy lab...

Hao Wu, et al. ∙

research

∙ 03/23/2021

Spatio-Temporal Sparsification for General Robust Graph Convolution Networks

Graph Neural Networks (GNNs) have attracted increasing attention due to ...

Mingming Lu, et al. ∙

research

∙ 03/09/2021

Uncertainty-aware Incremental Learning for Multi-organ Segmentation

Most existing approaches to train a unified multi-organ segmentation mod...

Yuhang Zhou, et al. ∙

research

∙ 03/09/2021

Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining

The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich ...

Jieneng Chen, et al. ∙

research

∙ 12/17/2020

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

We propose a novel method based on teacher-student learning framework fo...

Chenxin Xu, et al. ∙

research

∙ 12/15/2020

Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses

Point-Level temporal action localization (PTAL) aims to localize actions...

Chen Ju, et al. ∙

research

∙ 12/09/2020

ESAD: End-to-end Deep Semi-supervised Anomaly Detection

This paper explores semi-supervised anomaly detection, a more practical ...

Chaoqin Huang, et al. ∙

research

∙ 12/09/2020

Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework

Surrogate task based methods have recently shown great promise for unsup...

Fei Ye, et al. ∙

research

∙ 11/18/2020

Privileged Knowledge Distillation for Online Action Detection

Online Action Detection (OAD) in videos is proposed as a per-frame label...

Peisen Zhao, et al. ∙

research

∙ 11/03/2020

Learning on Attribute-Missing Graphs

Graphs with complete node attributes have been widely explored recently....

Xu Chen, et al. ∙

research

∙ 11/03/2020

Sampling and Recovery of Graph Signals based on Graph Neural Networks

We propose interpretable graph neural networks for sampling and recovery...

Siheng Chen, et al. ∙

research

∙ 10/13/2020

Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis

Differentiating Vertebral Compression Fractures (VCFs) associated with t...

Shixiang Feng, et al. ∙

research

∙ 10/13/2020

Universal Model for 3D Medical Image Analysis

Deep Learning-based methods recently have achieved remarkable progress i...

Xiaoman Zhang, et al. ∙

research

∙ 10/05/2020

Graph Cross Networks with Vertex Infomax Pooling

We propose a novel graph cross network (GXN) to achieve comprehensive fe...

Maosen Li, et al. ∙

research

∙ 09/17/2020

Urban Traffic Flow Forecast Based on FastGCRNN

Traffic forecasting is an important prerequisite for the application of ...

Ya Zhang, et al. ∙

research

∙ 09/15/2020

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

Cross domain recommendation (CDR) has been proposed to tackle the data s...

Xu Chen, et al. ∙

research

∙ 08/28/2020

Decoupled Variational Embedding for Signed Directed Networks

Node representation learning for signed directed networks has received c...

Xu Chen, et al. ∙

research

∙ 08/26/2020

Learning Robust Node Representation on Graphs

Graph neural networks (GNN), as a popular methodology for node represent...

Xu Chen, et al. ∙

research

∙ 07/16/2020

Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

Relational learning aims to make relation inference by exploiting the co...

Jingchao Su, et al. ∙

research

∙ 07/16/2020

Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph

Knowledge graphs (KGs) composed of users, objects, and tags are widely u...

Chenyang Li, et al. ∙

research

∙ 07/13/2020

Universal-to-Specific Framework for Complex Action Recognition

Video-based action recognition has recently attracted much attention in ...

Peisen Zhao, et al. ∙

research

∙ 04/11/2020

From Quantized DNNs to Quantizable DNNs

This paper proposes Quantizable DNNs, a special type of DNNs that can fl...

Kunyuan Du, et al. ∙

research

∙ 03/17/2020

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

We propose novel dynamic multiscale graph neural networks (DMGNN) to pre...

Maosen Li, et al. ∙

research

∙ 03/14/2020

Collaborative Motion Prediction via Neural Motion Message Passing

Motion prediction is essential and challenging for autonomous vehicles a...

Yue Hu, et al. ∙

research

∙ 02/18/2020

Constraining Temporal Relationship for Action Localization

Recently, temporal action localization (TAL), i.e., finding specific act...

Peisen Zhao, et al. ∙

research

∙ 11/25/2019

Inverse-Transform AutoEncoder for Anomaly Detection

Reconstruction-based methods have recently shown great promise for anoma...

Chaoqing Huang, et al. ∙

research

∙ 11/23/2019

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning

Existing automatic 3D image segmentation methods usually fail to meet th...

Xuan Liao, et al. ∙

research

∙ 10/17/2019

Cascading: Association Augmented Sequential Recommendation

Recently, recommendation according to sequential user behaviors has show...

Xu Chen, et al. ∙

research

∙ 10/05/2019

Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction

3D skeleton-based action recognition and motion prediction are two essen...

Maosen Li, et al. ∙

research

∙ 09/19/2019

Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data

Data augmentation has been widely applied as an effective methodology to...

Zhuoxun He, et al. ∙

Ya Zhang

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