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Found 139 papers, 74 papers with code
Perceptual Learned Video Compression with Recurrent Conditional GAN
The user study further validates the outstanding perceptual performance of PLVC in comparison with the latest learned video compression approaches and the official HEVC test model (HM 16. 20).
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 #1 on
Image Super-Resolution
on Manga109 - 4x upscaling
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.
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.
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.
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.
Generative Flows with Invertible Attentions
Flow-based generative models have shown excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations.
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
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.
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.
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.
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 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.
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 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 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 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 #246 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.
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.
Learning Accurate Dense Correspondences and When to Trust Them
Establishing dense correspondences between a pair of images is an important and general problem.
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.
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.
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.
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+.
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.
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.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
How to Train Your Energy-Based Model for Regression
While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking.
Ranked #1 on
Visual Object Tracking
on OTB-100
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low.
NTIRE 2020 Challenge on Image and Video Deblurring
This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning
Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent.
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former.
Ranked #1 on
Image-to-Image Translation
on Cityscapes Labels-to-Photo
(Per-pixel Accuracy metric)
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution.
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.
Probabilistic Regression for Visual Tracking
In this work, we therefore propose a probabilistic regression formulation and apply it to tracking.
Learning What to Learn for Video Object Segmentation
This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.
Know Your Surroundings: Exploiting Scene Information for Object Tracking
Such approaches are however prone to fail in case of e. g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking.
Deep Unfolding Network for Image Super-Resolution
As a result, the proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.
Self-Supervised 2D Image to 3D Shape Translation with Disentangled Representations
In this paper, we propose SIST, a Self-supervised Image to Shape Translation framework that fulfills three tasks: (i) reconstructing the 3D shape from a single image; (ii) learning disentangled representations for shape, appearance and viewpoint; and (iii) generating a realistic RGB image from these independent factors.
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 for Video Compression with Hierarchical Quality and Recurrent Enhancement
In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.
Replacing Mobile Camera ISP with a Single Deep Learning Model
The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation.
Efficient Video Semantic Segmentation with Labels Propagation and Refinement
(ii) On the GPU, two Convolutional Neural Networks: A main segmentation network that is used to predict dense semantic labels from scratch, and a Refiner that is designed to improve predictions from previous frames with the help of a fast Inconsistencies Attention Module (IAM).
Adversarial Feature Distribution Alignment for Semi-Supervised Learning
We test our method on CIFAR10 and SVHN.
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences.
Frequency Separation for Real-World Super-Resolution
Furthermore, we propose to separate the low and high image frequencies and treat them differently during training.
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
For training, only one set of source input images is therefore provided in the challenge.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
AIM 2019 Challenge on Image Demoireing: Dataset and Study
In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.
Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
AI Benchmark: All About Deep Learning on Smartphones in 2019
The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs.
Unsupervised Learning for Real-World Super-Resolution
Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image.
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation
Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature.
Efficient Video Super-Resolution through Recurrent Latent Space Propagation
However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms.
Ranked #2 on
Video Super-Resolution
on Vid4 - 4x upscaling
(PSNR metric)
Video Super-Resolution
Image and Video Processing
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.
Exemplar Guided Face Image Super-Resolution without Facial Landmarks
Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images.
Ranked #1 on
Image Super-Resolution
on VggFace2 - 8x upscaling
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.
Learning Discriminative Model Prediction for Tracking
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking.
Ranked #3 on
Visual Object Tracking
on TrackingNet
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution.
Fast Perceptual Image Enhancement
We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras.
SMIT: Stochastic Multi-Label Image-to-Image Translation
This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e. g., style) associated with the translation.
Towards Spectral Estimation from a Single RGB Image in the Wild
In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e. g. unknown camera parameters, unknown scene radiance, unknown scene contents).
Practical Full Resolution Learned Lossless Image Compression
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.
Ranked #2 on
Image Compression
on ImageNet32
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.
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago.
Night-to-Day Image Translation for Retrieval-based Localization
We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image.
The 2018 PIRM Challenge on Perceptual Image Super-resolution
This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018.
Adversarial Sampling for Active Learning
This paper proposes asal, a new GAN based active learning method that generates high entropy samples.
Towards Closing the Gap in Weakly Supervised Semantic Segmentation with DCNNs: Combining Local and Global Models
We reduce the gap by 64. 2% whereas the current state-of-the-art reduces it only by 57. 5%.
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks
Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.
O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images
Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years.
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.
An efficient CNN for spectral reconstruction from RGB images
Recently, the example-based single image spectral reconstruction from RGB images task, aka, spectral super-resolution was approached by means of deep learning by Galliani et al.
Generative Adversarial Networks for Extreme Learned Image Compression
We present a learned image compression system based on GANs, operating at extremely low bitrates.
Towards Image Understanding from Deep Compression without Decoding
Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods.
Conditional Probability Models for Deep Image Compression
During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.
ComboGAN: Unrestrained Scalability for Image Domain Translation
This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al.
Ranked #5 on
Facial Expression Translation
on CelebA
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks
We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training.
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.
Anchored Regression Networks Applied to Age Estimation and Super Resolution
We propose the Anchored Regression Network (ARN), a nonlinear regression network which can be seamlessly integrated into various networks or can be used stand-alone when the features have already been fixed.
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints.
Evaluating race and sex diversity in the world's largest companies using deep neural networks
Diversity is one of the fundamental properties for the survival of species, populations, and organizations.
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras.
On the Relation between Color Image Denoising and Classification
Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery.
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy.
Single Image Super Resolution - When Model Adaptation Matters
The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors.
Failure Detection for Facial Landmark Detectors
Most face applications depend heavily on the accuracy of the face and facial landmarks detectors employed.
Generic 3D Convolutional Fusion for image restoration
Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denois- ing and single image super-resolution.
k2-means for fast and accurate large scale clustering
k^2-means builds upon the standard k-means (Lloyd's algorithm) and combines a new strategy to accelerate the convergence with a new low time complexity divisive initialization.
Fast Optical Flow using Dense Inverse Search
Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity.
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning.
Ranked #40 on
Image Super-Resolution
on Set14 - 4x upscaling