Search Results for author: Yanfeng Wang

Found 13 papers, 1 papers with code

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

Cooperative Learning for Noisy Supervision

no code implementations11 Aug 2021 Hao Wu, Jiangchao Yao, Ya zhang, Yanfeng Wang

Learning with noisy labels has gained the enormous interest in the robust deep learning area.

Learning with noisy labels

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

no code implementations5 Aug 2021 Shixiang Feng, YuHang Zhou, Xiaoman Zhang, Ya zhang, Yanfeng Wang

A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network.

Knowledge Distillation

A Fourier-based Framework for Domain Generalization

1 code implementation CVPR 2021 Qinwei Xu, Ruipeng Zhang, Ya zhang, Yanfeng Wang, Qi Tian

Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data.

Data Augmentation Domain Generalization

H2O: A Benchmark for Visual Human-human Object Handover Analysis

no code implementations ICCV 2021 Ruolin Ye, Wenqiang Xu, Zhendong Xue, Tutian Tang, Yanfeng Wang, Cewu Lu

Besides, we also report the hand and object pose errors with existing baselines and show that the dataset can serve as the video demonstrations for robot imitation learning on the handover task.

Imitation Learning

Collaborative Label Correction via Entropy Thresholding

no code implementations31 Mar 2021 Hao Wu, Jiangchao Yao, Jiajie Wang, Yinru Chen, Ya zhang, Yanfeng Wang

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.

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

FDMT: A Benchmark Dataset for Fine-grained Domain Adaptation in Machine Translation

no code implementations31 Dec 2020 Wenhao Zhu, ShuJian Huang, Tong Pu, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

To motivate a wide investigation in such settings, we present a real-world fine-grained domain adaptation task in machine translation (FDMT).

Autonomous Vehicles Domain Adaptation +2

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

no code implementations15 Dec 2020 Chen Ju, Peisen Zhao, Ya zhang, Yanfeng Wang, Qi Tian

Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.

Weakly Supervised Action Localization

Privileged Knowledge Distillation for Online Action Detection

no code implementations18 Nov 2020 Peisen Zhao, Lingxi Xie, Ya zhang, Yanfeng Wang, Qi Tian

Knowledge distillation is employed to transfer the privileged information from the offline teacher to the online student.

Action Detection Curriculum Learning +1

SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation

no code implementations13 Oct 2020 Xiaoman Zhang, Shixiang Feng, YuHang Zhou, Ya zhang, Yanfeng Wang

We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tumor segmentation.

Brain Tumor Segmentation Self-Supervised Learning +2

Defending Adversarial Attacks by Correcting logits

no code implementations26 Jun 2019 Yifeng Li, Lingxi Xie, Ya zhang, Rui Zhang, Yanfeng Wang, Qi Tian

Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning.

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