Search Results for author:
Found 17 papers, 10 papers with code
SPIRONet: Spatial-Frequency Learning and Topological Channel Interaction Network for Vessel Segmentation
Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems.
A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
The developed multi-source UDA theory is theoretical and the generalization error on target subject is guaranteed.
Intent Detection
Multi-Source Unsupervised Domain Adaptation
+1
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
2. 5D-based segmentation models bridge computational efficiency of 2D-based models and spatial perception capabilities of 3D-based models.
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
This method trades off performance and robustness via introducing the robust Bellman operator into the algorithm.
CROP: Conservative Reward for Model-based Offline Policy Optimization
Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions.
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
The conservatism of current algorithms mostly relies on model uncertainty estimation.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG
On the DMS data set, GF-DANN has obtained an accuracy rate of 89. 47%, and the sensitivity and specificity are 90% and 89%.
Faster Person Re-Identification
In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification
Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images.
BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation
For the scale variation, our adaptive receptive field module aggregates multi-scale features and automatically fuses them with different weights.
Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments
LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution.
RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment
First, it can exploit pixel alignment and feature alignment jointly.
Cross-Modality Person Re-identification
Generative Adversarial Network
+2
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation.
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network
The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network.
Semi-Supervised Generative Adversarial Hashing for Image Retrieval
Secondly, we design novel structure of the generative model and the discriminative model to learn the distribution of triplet-wise information in a semi-supervised way.
