Search Results for author:
Found 7 papers, 4 papers with code
High-Resolution Swin Transformer for Automatic Medical Image Segmentation
Most of the existing Transformer-based networks for medical image segmentation are U-Net-like architecture that contains an encoder that utilizes a sequence of Transformer blocks to convert the input medical image from high-resolution representation into low-resolution feature maps and a decoder that gradually recovers the high-resolution representation from low-resolution feature maps.
Self-supervised Representation Learning for Evolutionary Neural Architecture Search
To enhance the predictive performance of neural predictors, we devise two self-supervised learning methods from different perspectives to pre-train the architecture embedding part of neural predictors to generate a meaningful representation of neural architectures.
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training.
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search
In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors.
GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering
To train the GATCluster in a completely unsupervised manner, we design four self-learning tasks with the constraints of transformation invariance, separability maximization, entropy analysis, and attention mapping.
AFO-TAD: Anchor-free One-Stage Detector for Temporal Action Detection
Many of the state-of-the-art methods predict the boundaries of action instances based on predetermined anchors akin to the two-dimensional object detection detectors.
DASNet: Reducing Pixel-level Annotations for Instance and Semantic Segmentation
Pixel-level annotation demands expensive human efforts and limits the performance of deep networks that usually benefits from more such training data.
