Search Results for author:
Found 12 papers, 3 papers with code
FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images.
Ranked #2 on
Few-Shot Semantic Segmentation
on COCO-20i (10-shot)
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization
Wide applications of differentiable two-player sequential games (e. g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms.
TransEM:Residual Swin-Transformer based regularized PET image reconstruction
Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received.
Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering
In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac).
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
Interpretable Image Recognition by Constructing Transparent Embedding Space
This plug-in embedding space is spanned by transparent basis concepts which are constructed on the Grassmann manifold.
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network
After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.
Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Positron emission tomography (PET) is widely used for clinical diagnosis.
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
Deep Representation Learning for Road Detection through Siamese Network
We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network.
Road Segmentation with Image-LiDAR Data Fusion
Robust road segmentation is a key challenge in self-driving research.
