126 citations · 472 across the 48 of their papers we have counts for
15 papers · 1 filter
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann +3
Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent…
Spatio-Temporal Graph Complementary Scattering Networks
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. To achieve effective analysis, spatio-t…
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Yujie Pan, Jiangchao Yao, Bo Han +3
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scena…
A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer
Jinxiang Liu, Yangheng Zhao, Siheng Chen +1
Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes imp…
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GN…
CaT: Weakly Supervised Object Detection with Category Transfer
Tianyue Cao, Lianyu Du, Xiaoyun Zhang +3
A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fu…
Cooperative Learning for Noisy Supervision
Hao Wu, Jiangchao Yao, Ya Zhang +1
Learning with noisy labels has gained the enormous interest in the robust deep learning area. Recent studies have empirically disclosed that utilizing dual networks can enhance the…
MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets
Shixiang Feng, Yuhang Zhou, Xiaoman Zhang +2
Annotating multiple organs in 3D medical images is time-consuming and costly. Meanwhile, there exist many single-organ datasets with one specific organ annotated. This paper invest…
Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning
Zida Cheng, Siheng Chen, Ya Zhang
3D hand-object pose estimation is an important issue to understand the interaction between human and environment. Current hand-object pose estimation methods require detailed 3D la…
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
Maosen Li, Siheng Chen, Yanning Shen +3
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative predictio…
A Fourier-based Framework for Domain Generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang +2
Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. Domain generalization aims at tackl…
Collaborative Label Correction via Entropy Thresholding
Hao Wu, Jiangchao Yao, Jiajie Wang +3
Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. W…
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
Chen Ju, Peisen Zhao, Siheng Chen +3
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incomp…
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
Mingming Lu, Ya Zhang
Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. However, recent studies have shown that adversa…
Uncertainty-aware Incremental Learning for Multi-organ Segmentation
Yuhang Zhou, Xiaoman Zhang, Shixiang Feng +2
Most existing approaches to train a unified multi-organ segmentation model from several single-organ datasets require simultaneously access multiple datasets during training. In th…