36 citations · 91 across the 10 of their papers we have counts for
10 papers
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training
Zhenglun Kong, Haoyu Ma, Geng Yuan +12
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit the…
PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation
Haoyu Ma, Zhe Wang, Yifei Chen +6
Recently, the vision transformer and its variants have played an increasingly important role in both monocular and multi-view human pose estimation. Considering image patches as to…
Medical Image Registration via Neural Fields
Shanlin Sun, Kun Han, Chenyu You +8
Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal defo…
Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow
Shanlin Sun, Kun Han, Deying Kong +3
Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed distance functions learned through deep neural nets. Recently DIFs-based methods have been proposed to h…
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation
Xiangyi Yan, Hao Tang, Shanlin Sun +3
Recent advances in transformer-based models have drawn attention to exploring these techniques in medical image segmentation, especially in conjunction with the U-Net model (or its…
TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
Haoyu Ma, Liangjian Chen, Deying Kong +7
Estimating the 2D human poses in each view is typically the first step in calibrated multi-view 3D pose estimation. But the performance of 2D pose detectors suffers from challengin…
Recurrent Mask Refinement for Few-Shot Medical Image Segmentation
Hao Tang, Xingwei Liu, Shanlin Sun +2
Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and ar…
Undistillable: Making A Nasty Teacher That CANNOT teach students
Haoyu Ma, Tianlong Chen, Ting-Kuei Hu +3
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models. However, in certain situa…
Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
Xiaoran Xu, Wei Feng, Yunsheng Jiang +3
We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an…
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Mengyi Liu, Xiaohui Xie, Hanning Zhou
Video relevance prediction is one of the most important tasks for online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized…