Search Results for author:
Found 27 papers, 20 papers with code
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
The effectiveness of the method is also demonstrated on the real-world SR setting.
Reference-based Image Super-Resolution with Deformable Attention Transformer
Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Revisiting Random Channel Pruning for Neural Network Compression
The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning.
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced.
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.
Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer.
VRT: A Video Restoration Transformer
Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.
Ranked #1 on
Deblurring
on BASED
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
We propose a method to identify features with predictive information in the input domain.
Video Super-Resolution Transformer
Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.
LocalViT: Bringing Locality to Vision Transformers
The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept.
Ranked #540 on
Image Classification
on ImageNet
Towards Efficient Graph Convolutional Networks for Point Cloud Handling
In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds.
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays.
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network
State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Plug-and-Play Image Restoration with Deep Denoiser Prior
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i. e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner.
DHP: Differentiable Meta Pruning via HyperNetworks
Passing the sparsified latent vectors through the hypernetworks, the corresponding slices of the generated weight parameters can be removed, achieving the effect of network pruning.
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.
Learning Filter Basis for Convolutional Neural Network Compression
Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.
3D Appearance Super-Resolution with Deep Learning
Experimental results demonstrate that our proposed networks successfully incorporate the 3D geometric information and super-resolve the texture maps.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Word Recognition with Deep Conditional Random Fields
On the other hand, word recognition is a sequential problem where we need to model the correlation between characters.
Joint Visual Denoising and Classification using Deep Learning
Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model.
