research
∙
09/05/2023
Probabilistic Self-supervised Learning via Scoring Rules Minimization
In this paper, we propose a novel probabilistic self-supervised learning...
share
research
∙
07/15/2023
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation
Intelligent edge vision tasks encounter the critical challenge of ensuri...
research
∙
03/01/2023
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
The aim of this paper is to propose a mechanism to efficiently and expli...
research
∙
07/25/2022
Reference-based Image Super-Resolution with Deformable Attention Transformer
Reference-based image super-resolution (RefSR) aims to exploit auxiliary...
research
∙
05/11/2022
Revisiting Random Channel Pruning for Neural Network Compression
Channel (or 3D filter) pruning serves as an effective way to accelerate ...
research
∙
03/24/2022
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep ...
research
∙
03/05/2022
Machine Learning Applications in Diagnosis, Treatment and Prognosis of Lung Cancer
The recent development of imaging and sequencing technologies enables sy...
research
∙
01/28/2022
VRT: A Video Restoration Transformer
Video restoration (e.g., video super-resolution) aims to restore high-qu...
research
∙
10/04/2021
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
One principal approach for illuminating a black-box neural network is fe...
research
∙
06/12/2021
Video Super-Resolution Transformer
Video super-resolution (VSR), with the aim to restore a high-resolution ...
research
∙
04/12/2021
LocalViT: Bringing Locality to Vision Transformers
We study how to introduce locality mechanisms into vision transformers. ...
research
∙
04/12/2021
Towards Efficient Graph Convolutional Networks for Point Cloud Handling
In this paper, we aim at improving the computational efficiency of graph...
research
∙
04/01/2021
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Neural networks have demonstrated remarkable performance in classificati...
research
∙
12/15/2020
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, w...
research
∙
09/15/2020
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image supe...
research
∙
08/31/2020
Plug-and-Play Image Restoration with Deep Denoiser Prior
Recent works on plug-and-play image restoration have shown that a denois...
research
∙
06/29/2020
The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture
In this paper, we tackle the problem of convolutional neural network des...
research
∙
04/02/2020
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
These days, unsupervised super-resolution (SR) has been soaring due to i...
research
∙
03/30/2020
DHP: Differentiable Meta Pruning via HyperNetworks
Network pruning has been the driving force for the efficient inference o...
research
∙
03/19/2020
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
In this paper, we analyze two popular network compression techniques, i....
research
∙
08/23/2019
Learning Filter Basis for Convolutional Neural Network Compression
Convolutional neural networks (CNNs) based solutions have achieved state...
research
∙
06/03/2019
3D Appearance Super-Resolution with Deep Learning
We tackle the problem of retrieving high-resolution (HR) texture maps of...
research
∙
10/03/2018
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enh...
research
∙
12/04/2016
Joint Visual Denoising and Classification using Deep Learning
Visual restoration and recognition are traditionally addressed in pipeli...
research
∙
12/04/2016
