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Found 45 papers, 12 papers with code
Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective
The realm of classical phase retrieval concerns itself with the arduous task of recovering a signal from its Fourier magnitude measurements, which are fraught with inherent ambiguities.
Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image.
Multi-Scale Prototypical Transformer for Whole Slide Image Classification
The PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture.
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation
In this paper, we propose a hybrid densely connected network for tumor segmentation, named H-DenseFormer, which combines the representational power of the Convolutional Neural Network (CNN) and the Transformer structures.
Q-YOLO: Efficient Inference for Real-time Object Detection
Real-time object detection plays a vital role in various computer vision applications.
Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
To this end, in this paper, we propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction, which focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner.
Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images
To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD for breast cancers.
Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification
The key idea of MEGT is to adopt two independent Efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i. e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM).
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine.
Fast MRI Reconstruction via Edge Attention
To solve this problem, we propose a lightweight and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct images with edge guidance.
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges.
SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration
In this paper, we introduce an effective hybrid architecture for sand image restoration tasks, which leverages local features from CNN and long-range dependencies captured by transformer to improve the results further.
Adverse Weather Removal with Codebook Priors
Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal.
Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR
To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation.
Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study
Adhering to it, a model-based deep learning network is designed to restore the image.
Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework
Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well.
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging
To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.
Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network
Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps.
Two Dimensional Sparse-Regularization-Based InSAR Imaging with Back-Projection Embedding
First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded.
Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution
To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed.
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System
In this paper, a new distributed mmW radar system is designed to solve this problem.
Constant-Time-Delay Interferences In Near-Field SAR: Analysis And Suppression In Image Domain
To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work.
Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement
Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy.
Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis
In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis.
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc.
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images
In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models.
A channel attention based MLP-Mixer network for motor imagery decoding with EEG
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task.
Causal Incremental Graph Convolution for Recommender System Retraining
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
Incremental Learning for Personalized Recommender Systems
Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment.
Logit Attenuating Weight Normalization
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems.
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training.
Task-driven Semantic Coding via Reinforcement Learning
However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process.
DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images
The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy.
Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions
While, it is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database that consists of histopathological whole slide images (WSIs) for a query ROI.
SRA-LSTM: Social Relationship Attention LSTM for Human Trajectory Prediction
Pedestrian trajectory prediction for surveillance video is one of the important research topics in the field of computer vision and a key technology of intelligent surveillance systems.
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography
In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammography-based CAD.
Reconstruction of Quantitative Susceptibility Maps from Phase of Susceptibility Weighted Imaging with Cross-Connected $Ψ$-Net
In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $\Psi$-Net (C$\Psi$-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing.
Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images
Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.
SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration
Accelerating the inference speed of CNNs is critical to their deployment in real-world applications.
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics
Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc.
Lightweight Image Super-Resolution with Adaptive Weighted Learning Network
PyTorch code for our paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network"
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
