2.6k citations · 4.6k across the 107 of their papers we have counts for
13 papers · 1 filter
Point Cloud Processing via Recurrent Set Encoding
Pengxiang Wu, Chao Chen, Jingru Yi +1
We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an un…
Object-Guided Instance Segmentation for Biological Images
Jingru Yi, Hui Tang, Pengxiang Wu +5
Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the ob…
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Ligong Han, Ruijiang Gao, Mun Kim +3
Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address th…
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian, Long Zhao, Xi Peng +1
Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which…
ASSD: Attentive Single Shot Multibox Detector
Jingru Yi, Pengxiang Wu, Dimitris N. Metaxas
This paper proposes a new deep neural network for object detection. The proposed network, termed ASSD, builds feature relations in the spatial space of the feature map. With the gl…
2nd Place Solution to the GQA Challenge 2019
Shijie Geng, Ji Zhang, Hang Zhang +2
We present a simple method that achieves unexpectedly superior performance for Complex Reasoning involved Visual Question Answering. Our solution collects statistical features from…
Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
Chaowei Tan, Zhennan Yan, Shaoting Zhang +2
The 3D morphology and quantitative assessment of knee articular cartilages (i.e., femoral, tibial, and patellar cartilage) in magnetic resonance (MR) imaging is of great importance…
Greedy AutoAugment
Alireza Naghizadeh, Mohammadsajad Abavisani, Dimitris N. Metaxas
A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentatio…
Unsupervised Domain Adaptation via Calibrating Uncertainties
Ligong Han, Yang Zou, Ruijiang Gao +2
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset. Intuitively, a model trained on source domai…
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
Jingru Yi, Pengxiang Wu, Qiaoying Huang +4
Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells du…
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
Yuxiao Chen, Long Zhao, Xi Peng +2
We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand ske…
Semantic Graph Convolutional Networks for 3D Human Pose Regression
Long Zhao, Xi Peng, Yu Tian +2
In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convol…
Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data
Xiaoxiao He, Chaowei Tan, Yuting Qiao +3
For the initial shoulder preoperative diagnosis, it is essential to obtain a three-dimensional (3D) bone mask from medical images, e.g., magnetic resonance (MR). However, obtaining…