Search Results for author:
Found 12 papers, 5 papers with code
Exploring Generalizable Distillation for Efficient Medical Image Segmentation
Considering the domain-invariant representative vectors in MSAN, we propose two generalizable knowledge distillation schemes for cross-domain distillation, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD).
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning
To preserve the generated faces being more structure-coordinated, the IRSG models inter-class structural relations among every facial component by graph representation learning.
Generative Adversarial Network
Graph Representation Learning
+1
Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints.
Ranked #17 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network
To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting.
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition
However, the complexity of the State-Of-The-Art (SOTA) models of this task tends to be exceedingly sophisticated and over-parameterized, where the low efficiency in model training and inference has obstructed the development in the field, especially for large-scale action datasets.
Ranked #25 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention.
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
Ranked #43 on
Skeleton Based Action Recognition
on NTU RGB+D 120
CANet: Context Aware Network for 3D Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
Medical Instrument Detection in Ultrasound-Guided Interventions: A Review
Medical instrument detection is essential for computer-assisted interventions since it would facilitate the surgeons to find the instrument efficiently with a better interpretation, which leads to a better outcome.
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions.
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention.
