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Found 189 papers, 108 papers with code
Audio-Visual Efficient Conformer for Robust Speech Recognition
We improve previous lip reading methods using an Efficient Conformer back-end on top of a ResNet-18 visual front-end and by adding intermediate CTC losses between blocks.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' ``pop-out'' prior in 3D.
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.
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network.
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
In the second stage, the LT-based global fusion and INN-based local fusion layers output the fused image.
Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices
We propose a lightweight model for blind UDC Image Restoration and HDR, and we also provide a benchmark comparing the performance and runtime of different methods on smartphones.
Advancing Learned Video Compression with In-loop Frame Prediction
In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate.
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity.
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks
The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
Perceptual Image Enhancement for Smartphone Real-Time Applications
Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.
SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction
NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i. e., reconstructing real-world scenes and registering camera parameters simultaneously.
Basic Binary Convolution Unit for Binarized Image Restoration Network
In this study, we reconsider components in binary convolution, such as residual connection, BatchNorm, activation function, and structure, for IR tasks.
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
3D-Aware Video Generation
Generative models have emerged as an essential building block for many image synthesis and editing tasks.
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
Ranked #1 on
Deblurring
on DVD
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement.
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.
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.
Conformer and Blind Noisy Students for Improved Image Quality Assessment
Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately.
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).
Arbitrary-Scale Image Synthesis
Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales.
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.
Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild
We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone.
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i. e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement.
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features.
Fast Online Video Super-Resolution with Deformable Attention Pyramid
Our DAP aligns and integrates information from the recurrent state into the current frame prediction.
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
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.
Flow-Guided Sparse Transformer for Video Deblurring
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring.
Ranked #1 on
Deblurring
on DVD
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model
We propose a novel framework, i. e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations.
Global and Local Alignment Networks for Unpaired Image-to-Image Translation
The global alignment network aims to transfer the input image from the source domain to the target domain.
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
The HSI representations are highly similar and correlated across the spectral dimension.
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution
Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions.
Towards Flexible Blind JPEG Artifacts Removal
Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage.
Best Practices in Pool-based Active Learning for Image Classification
We emphasize aspects such as the importance of using data augmentation, the need of separating the contribution of a classification network and the acquisition strategy to the overall performance, the advantages that a proper initialization of the network can bring to AL.
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
In order to apply dense methods to real-world applications, such as pose estimation, image manipulation, or 3D reconstruction, it is therefore crucial to estimate the confidence of the predicted matches.
Perceptual Learned Video Compression with Recurrent Conditional GAN
This paper proposes a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional GAN.
Generalized Real-World Super-Resolution through Adversarial Robustness
Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model's weaknesses.
SwinIR: Image Restoration Using Swin Transformer
In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
Ranked #2 on
Grayscale Image Denoising
on Urban100 sigma15
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction.
Ranked #4 on
Burst Image Super-Resolution
on BurstSR
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously.
NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR.
Deep Homography for Efficient Stereo Image Compression
To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i. e., H matrix.
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
Generative Flows with Invertible Attentions
The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows.
NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results
This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021.
Fourier Space Losses for Efficient Perceptual Image Super-Resolution
As large models are often not practical in real-world applications, we investigate and propose novel loss functions, to enable SR with high perceptual quality from much more efficient models.
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices.
Fast and Accurate Camera Scene Detection on Smartphones
AI-powered automatic camera scene detection mode is nowadays available in nearly any modern smartphone, though the problem of accurate scene prediction has not yet been addressed by the research community.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
NTIRE 2021 Challenge on Image Deblurring
In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions.
NTIRE 2021 Challenge on Video Super-Resolution
Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart.
NTIRE 2021 Depth Guided Image Relighting Challenge
In this paper, we review the NTIRE 2021 depth guided image relighting challenge.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study
In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
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.
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 #521 on
Image Classification
on ImageNet
Flow-based Kernel Prior with Application to Blind Super-Resolution
Kernel estimation is generally one of the key problems for blind image super-resolution (SR).
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods.
Deep Burst Super-Resolution
We propose a novel architecture for the burst super-resolution task.
Ranked #6 on
Burst Image Super-Resolution
on BurstSR
Burst Image Super-Resolution
Multi-Frame Super-Resolution
+2
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
Modern solutions to the single image super-resolution (SISR) problem using deep neural networks aim not only at better performance accuracy but also at a lighter and computationally efficient model.
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
We propose DeFlow, a method for learning stochastic image degradations from unpaired data.
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
This effectively limits the performance and generalization capabilities of existing video segmentation methods.
Local Memory Attention for Fast Video Semantic Segmentation
Our approach aggregates a rich representation of the semantic information in past frames into a memory module.
Learning Accurate Dense Correspondences and When to Trust Them
Establishing dense correspondences between a pair of images is an important and general problem.
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.
An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain.
Zero-Pair Image to Image Translation using Domain Conditional Normalization
In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i. e., translating between two domains which have no paired training data available but each have paired training data with a third domain.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
A Weakly Supervised Convolutional Network for Change Segmentation and Classification
The core part of our model, the Change Segmentation and Classification (CSC) module, learns an accurate change mask at a hidden layer by using a custom Remapping Block and then segmenting the current input image with the change mask.
Self-Supervised Shadow Removal
Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents.
SMILE: Semantically-guided Multi-attribute Image and Layout Editing
Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.
AIM 2020 Challenge on Image Extreme Inpainting
This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting.
Fast Few-Shot Classification by Few-Iteration Meta-Learning
By employing an efficient initialization module and a Steepest Descent based optimization algorithm, our base learner predicts a powerful classifier within only a few iterations.
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search
Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks.
AIM 2020 Challenge on Video Temporal Super-Resolution
Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recordedframe rate is low.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
Learning to Improve Image Compression without Changing the Standard Decoder
Therefore, we propose learning to improve the encoding performance with the standard decoder.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
We propose GOCor, a fully differentiable dense matching module, acting as a direct replacement to the feature correlation layer.
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.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
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.
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding to the VSR techniques based on deep learning.
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions.
Ranked #1 on
Vector Graphics Animation
on SVG-Icons8
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.
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
Rendering Natural Camera Bokeh Effect with Deep Learning
Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas.
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network
We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings.
Learning Context-Based Non-local Entropy Modeling for Image Compression
To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images
The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates the real conditions of hazy scenes.
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
This paper reviews the second challenge on spectral reconstruction from RGB images, i. e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.