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Found 28 papers, 12 papers with code
Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph
In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant.
TRG-Net: An Interpretable and Controllable Rain Generator
Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks compared to current state-of-the-art rain generation methods.
Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration
In current deep unfolding methods, such a proximal network is generally designed as a CNN architecture, whose necessity has been proven by a recent theory.
RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation
Retinal vessel segmentation is of great clinical significance for the diagnosis of many eye-related diseases, but it is still a formidable challenge due to the intricate vascular morphology.
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment.
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images.
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Deep generative models have achieved tremendous success in designing novel drug molecules in recent years.
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment
Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance.
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image.
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
It has been shown that equivariant convolution is very helpful for many types of computer vision tasks.
RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining
To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability.
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise
A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements.
From Rain Generation to Rain Removal
For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets.
Learning to Purify Noisy Labels via Meta Soft Label Corrector
By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, we could adaptively obtain rectified soft labels iteratively according to current training problems without manually preset hyper-parameters.
Structural Residual Learning for Single Image Rain Removal
Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages.
A Model-driven Deep Neural Network for Single Image Rain Removal
Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model.
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance.
Ranked #24 on
Image Classification
on Clothing1M
(using extra training data)
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image.
Tools and Benchmarks for Automated Log Parsing
Logs are imperative in the development and maintenance process of many software systems.
Software Engineering
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
The 3-D total variation (3DTV) is a powerful regularization term, which encodes the local smoothness prior structure underlying a hyper-spectral image (HSI), for general HSI processing tasks.
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement
To address this issue, we propose an unsupervised DL method for LdCT enhancement that incorporates unlabeled LdCT sinograms directly into the network training.
Video Rain Streak Removal by Multiscale Convolutional Sparse Coding
Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.
Should We Encode Rain Streaks in Video as Deterministic or Stochastic?
Videos taken in the wild sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks.
Self-Paced Co-training
During co-training process, labels of unlabeled instances in the training pool are very likely to be false especially in the initial training rounds, while the standard co-training algorithm utilizes a “draw without replacement” manner and does not remove these false labeled instances from training.
Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization
Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks.
Convolutional Sparse Coding for Image Super-Resolution
Sparse coding (SC) plays an important role in versatile computer vision applications such as image super-resolution (SR).
A Novel Sparsity Measure for Tensor Recovery
In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor.
On the Optimal Solution of Weighted Nuclear Norm Minimization
Nevertheless, so far the global optimal solution of WNNM problem is not completely solved yet due to its non-convexity in general cases.
