Search Results for author:
Found 207 papers, 126 papers with code
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i. e., UNIC).
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
To evaluate our proposed MetaF2N, we have collected a real-world low-quality dataset with one or multiple faces in each image, and our MetaF2N achieves superior performance on both synthetic and real-world datasets.
Aggregating Long-term Sharp Features via Hybrid Transformers for Video Deblurring
To aggregate long-term sharp features from detected sharp frames, we utilize a global Transformer with multi-scale matching capability.
Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration
To empower the framework for eliminating diverse degradations, we devise a Sequence-wise Adaptive Degradation Estimator (SADE) to estimate degradation features for the input corrupted video.
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
In this paper, we propose a VQGAN-based framework (i. e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement.
UniM$^2$AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving
The extension makes it possible to back-project the informative features, obtained by fusing features from both modalities, into their native modalities to reconstruct the multiple masked inputs.
Rethinking Client Drift in Federated Learning: A Logit Perspective
Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT).
Towards Instance-adaptive Inference for Federated Learning
However, the performance of the global model is often hampered by non-i. i. d.
Data-free Black-box Attack based on Diffusion Model
Since the training data for the target model in a data-free black-box attack is not available, most recent schemes utilize GANs to generate data for training substitute model.
Improving Transferability of Adversarial Examples via Bayesian Attacks
Our ICLR work advocated for enhancing transferability in adversarial examples by incorporating a Bayesian formulation into model parameters, which effectively emulates the ensemble of infinitely many deep neural networks, while, in this paper, we introduce a novel extension by incorporating the Bayesian formulation into the model input as well, enabling the joint diversification of both the model input and model parameters.
DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration
With the two cascaded subtasks, DRM-IR first dynamically models the task-specific degradation based on a reference image pair and further restores the image with the collected degradation statistics.
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it.
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
Thus, we further propose an indicator to measure the local similarity of models, called robustness similitude.
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions.
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map.
ControlVideo: Training-free Controllable Text-to-Video Generation
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling.
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
In particular, the proposed method, named intermediate-level perturbation decay (ILPD), encourages the intermediate-level perturbation to be in an effective adversarial direction and to possess a great magnitude simultaneously.
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs.
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
Learning Generative Structure Prior for Blind Text Image Super-resolution
To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook.
Towards Universal Vision-language Omni-supervised Segmentation
To improve the open-world segmentation ability, we leverage omni-supervised data (i. e., panoptic segmentation data, object detection data, and image-text pairs data) into training, thus enriching the open-world segmentation ability and achieving better segmentation accuracy.
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation.
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
In this paper, by contrast, we opt for the diversity in substitute models and advocate to attack a Bayesian model for achieving desirable transferability.
Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography
Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling.
Ranked #1 on
Image Denoising
on ELD SonyA7S2 x200
NUWA-LIP: Language-Guided Image Inpainting With Defect-Free VQGAN
Language-guided image inpainting aims to fill the defective regions of an image under the guidance of text while keeping the non-defective regions unchanged.
Position-Aware Contrastive Alignment for Referring Image Segmentation
The main challenge of this task is to understand the visual and linguistic content simultaneously and to find the referred object accurately among all instances in the image.
HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping
We blend the semantic layouts of source head and source body, and then inpaint the transition region by the semantic layout generator, achieving a coarse-grained head swapping.
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results.
Relationship Quantification of Image Degradations
To tackle the first challenge, we proposed a Degradation Relationship Index (DRI) which is defined as the mean drop rate difference in the validation loss between two models which are respectively trained using the anchor degradation and the mixture of the anchor and the auxiliary degradations.
Learning Single Image Defocus Deblurring with Misaligned Training Pairs
First, in the deblurring module, a bi-directional optical flow-based deformation is introduced to tolerate spatial misalignment between deblurred and ground-truth images.
Texts as Images in Prompt Tuning for Multi-Label Image Recognition
Nonetheless, visual data (e. g., images) is by default prerequisite for learning prompts in existing methods.
Contrastive Learning
Multi-label Image Recognition with Partial Labels
Self-Supervised Image Restoration with Blurry and Noisy Pairs
By learning in a collaborative manner, the deblurring and denoising tasks in our method can benefit each other.
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).
Learning Dual Memory Dictionaries for Blind Face Restoration
Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model.
ImaginaryNet: Learning Object Detectors without Real Images and Annotations
Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model deployed to generate a photo-realistic image.
LPT: Long-tailed Prompt Tuning for Image Classification
For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into target domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with discrimination ability.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
(using extra training data)
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors.
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification.
Ranked #3 on
Training-free 3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
A heterogeneous group CNN for image super-resolution
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
Multi-stage image denoising with the wavelet transform
In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i. e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB).
Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement
In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase.
Ranked #7 on
Image Dehazing
on SOTS Indoor
Two-Stream Networks for Object Segmentation in Videos
The compact instance stream effectively improves the segmentation accuracy of the unseen pixels, while fusing two streams with the adaptive routing map leads to an overall performance boost.
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Generally, with given pseudo ground-truths generated from the well-trained WSOD network, we propose a two-module iterative training algorithm to refine pseudo labels and supervise better object detector progressively.
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality.
Adversarial Contrastive Learning via Asymmetric InfoNCE
Contrastive learning (CL) has recently been applied to adversarial learning tasks.
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
In this paper, we present a novel one-shot generative domain adaption method, i. e., DiFa, for diverse generation and faithful adaptation.
Symmetry-Aware Transformer-based Mirror Detection
Mirror detection aims to identify the mirror regions in the given input image.
Learning Diverse Tone Styles for Image Retouching
In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass.
An Improved Normed-Deformable Convolution for Crowd Counting
However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information.
Robust Deep Ensemble Method for Real-world Image Denoising
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.
Image Super-resolution with An Enhanced Group Convolutional Neural Network
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR).
Squeeze Training for Adversarial Robustness
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community.
Generative Adversarial Networks for Image Super-Resolution: A Survey
Second, we present popular architectures for GANs in big and small samples for image applications.
Learning Dual-Pixel Alignment for Defocus Deblurring
Generally, DPANet is an encoder-decoder with skip-connections, where two branches with shared parameters in the encoder are employed to extract and align deep features from left and right views, and one decoder is adopted to fuse aligned features for predicting the sharp image.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Particularly, by treating all labeled data as positive samples, PU learning is leveraged to identify negative samples (i. e., outliers) from unlabeled data.
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption.
Localization Distillation for Object Detection
Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking underperforms for years.
Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis
Semantic image synthesis is a challenging task with many practical applications.
Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective
Meanwhile, diverse testing sets are also provided with different types of reflection and scenes.
Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis
Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly inferred from an object's shape.
An Intermediate-level Attack Framework on The Basis of Linear Regression
This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples.
Self-Promoted Supervision for Few-Shot Transformer
The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired.
On Steering Multi-Annotations per Sample for Multi-Task Learning
Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored.
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch.
NÜWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN
Language guided image inpainting aims to fill in the defective regions of an image under the guidance of text while keeping non-defective regions unchanged.
Invertible Network for Unpaired Low-light Image Enhancement
Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately.
Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds
We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range.
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc.
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it.
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation
It simply encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation.
Local Patch Network with Global Attention for Infrared Small Target Detection
A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset.
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
VirFace: Enhancing Face Recognition via Unlabeled Shallow Data
In this paper, we propose a novel face recognition method, named VirFace, to effectively apply the unlabeled shallow data for face recognition.
Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps
In this paper, we present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes with several distinct merits.
Learning Semantic Person Image Generation by Region-Adaptive Normalization
In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style.
Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Asymmetric CNN for image super-resolution
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution.
Deepfake Forensics via An Adversarial Game
In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities.
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images.
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
For learning pseudo mask generator from the auxiliary dataset, we present a bi-level optimization formulation.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Localization Distillation for Dense Object Detection
Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.
Self Sparse Generative Adversarial Networks
In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem.
Hybrid Trilinear and Bilinear Programming for Aligning Partially Overlapping Point Sets
The resulting lower bound problem has the merit that it can be efficiently solved via linear assignment and low dimensional convex quadratic programming.
Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames
EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods.
Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance
To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate.
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.
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
To cope with this issue, this paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training (PTMWRN) scheme to improve image denoising performance.
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.
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations.
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.
Unpaired Learning of Deep Image Denoising
As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set.
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision
Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pose and illumination discrepancy during training.
Component Divide-and-Conquer for Real-World Image Super-Resolution
Learning an SR model with conventional pixel-wise loss usually is easily dominated by flat regions and edges, and fails to infer realistic details of complex textures.
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block.
Designing and Training of A Dual CNN for Image Denoising
The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.
Lightweight image super-resolution with enhanced CNN
Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image.
Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm
Our method is also $\epsilon-$globally optimal and thus is guaranteed to be robust.
Cross-Scale Internal Graph Neural Network for Image Super-Resolution
Specifically, we dynamically construct a cross-scale graph by searching k-nearest neighboring patches in the downsampled LR image for each query patch in the LR image.
Flexible Image Denoising with Multi-layer Conditional Feature Modulation
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model.
Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring
In this work, we propose a Dark and Bright Channel Priors embedded Network (DBCPeNet) to plug the channel priors into a neural network for effective dynamic scene deblurring.
Ranked #25 on
Image Deblurring
on GoPro
(using extra training data)
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+.
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
Deep Adaptive Inference Networks for Single Image Super-Resolution
Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easier regions with less details and the scenarios with restricted efficiency constraints.
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content
First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
Ranked #4 on
Virtual Try-on
on VITON
(IS metric)
Deep Learning on Image Denoising: An overview
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity.
Ranked #691 on
Image Classification
on ImageNet
Perspective-Guided Convolution Networks for Crowd Counting
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i. e. PGCNet), which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect.
Image denoising using deep CNN with batch renormalization
In this paper, we report the design of a novel network called a batch-renormalization denoising network (BRDNet).
Image Inpainting with Learnable Bidirectional Attention Maps
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness.
Ranked #2 on
Image Inpainting
on Paris StreetView
Deep Concept-wise Temporal Convolutional Networks for Action Localization
In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution.
Neural Blind Deconvolution Using Deep Priors
To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.
Multi-level Wavelet Convolutional Neural Networks
Specifically, MWCNN for image restoration is based on U-Net architecture, and inverse wavelet transform (IWT) is deployed to reconstruct the high resolution (HR) feature maps.
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance.
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization.
Spatio-Temporal Filter Adaptive Network for Video Deblurring
To overcome the limitation of separate optical flow estimation, we propose a Spatio-Temporal Filter Adaptive Network (STFAN) for the alignment and deblurring in a unified framework.
Ranked #3 on
Deblurring
on DVD
(using extra training data)
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks.
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels
Convolutional neural networks have been proven effective in a variety of image restoration tasks.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
The proposed methods are highly modular, readily plugged into existing deep CNNs.
Ranked #1 on
Image Classification
on iNaturalist
(Top 3 Error metric)
DAVANet: Stereo Deblurring with View Aggregation
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles.
Blind Super-Resolution With Iterative Kernel Correction
In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown.
Ranked #2 on
Blind Super-Resolution
on Set5 - 3x upscaling
Learning Content-Weighted Deep Image Compression
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance.
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels.
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
The merits of the proposed MCTL are four-fold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and domain adaptation is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric (GGDM) is presented, such that both the global and local discrepancy can be effectively and positively reduced; 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario.
Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
In this work, we propose an Extreme Channel Prior embedded Network (ECPeNet) to plug the extreme channel priors (i. e., priors on dark and bright channels) into a network architecture for effective dynamic scene deblurring.
Progressive Image Deraining Networks: A Better and Simpler Baseline
To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.
Ranked #1 on
Single Image Deraining
on Rain1400
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.
Learning Symmetry Consistent Deep CNNs for Face Completion
As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures.
Ranked #1 on
Facial Inpainting
on VggFace2
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework.
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks
However, both GAP and existing HOP methods assume unimodal distributions, which cannot fully capture statistics of convolutional activations, limiting representation ability of deep CNNs, especially for samples with complex contents.
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians
Different from single view case, MSL should take both common and specific knowledge among different views into consideration.
Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior
Video summarization is a challenging under-constrained problem because the underlying summary of a single video strongly depends on users' subjective understandings.
Convolutional Neural Networks based Intra Prediction for HEVC
To overcome this problem, in this paper, an intra prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block.
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.
Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking
In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an end-to-end manner.
Scaled Simplex Representation for Subspace Clustering
However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations.
Identity Preserving Face Completion for Large Ocular Region Occlusion
Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity.
Toward Convolutional Blind Denoising of Real Photographs
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Ranked #3 on
Denoising
on Darmstadt Noise Dataset
Generative Adversarial Learning Towards Fast Weakly Supervised Detection
Without the need of annotating bounding boxes, the existing methods usually follow a two/multi-stage pipeline with an online compulsive stage to extract object proposals, which is an order of magnitude slower than fast fully supervised object detectors such as SSD [31] and YOLO [34].