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Found 66 papers, 34 papers with code
HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing
In this paper, we present a Haze-Aware Representation Distillation Generative Adversarial Network named HardGAN for single-image dehazing.
LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection
However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data.
Make Graph-based Referring Expression Comprehension Great Again through Expression-guided Dynamic Gating and Regression
Extensive experimental results on the RefCOCO, RefCOCO+, RefCOCOg, Flickr30K, RefClef, and Ref-reasoning datasets demonstrate the effectiveness of the DGC module and the EGR strategy in consistently boosting the performances of various graph-based REC methods.
Towards Robust Online Domain Adaptive Semantic Segmentation under Adverse Weather Conditions
Online Domain Adaptation (OnDA) is designed to handle unforeseeable domain changes at minimal cost that occur during the deployment of the model, lacking clear boundaries between the domain, such as sudden weather events.
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution
Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks.
Domain-adaptive Video Deblurring via Test-time Blurring
Next, we utilize a blurring model to produce blurred images based on the pseudo-sharp images extracted during testing.
Heterogeneous window transformer for image denoising
To make a tradeoff between distance modeling and denoising time, we propose a heterogeneous window transformer (HWformer) for image denoising.
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource
As a contrast to off-the-shelf methods that solve SR tasks across various scales with the same computing costs, our AnySR innovates in: 1) building arbitrary-scale tasks as any-resource implementation, reducing resource requirements for smaller scales without additional parameters; 2) enhancing any-scale performance in a feature-interweaving fashion, inserting scale pairs into features at regular intervals and ensuring correct feature/scale processing.
Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution
To alleviate these issues, we develop the first attempt to integrate the Vision State Space Model (Mamba) for RSI-SR, which specializes in processing large-scale RSI by capturing long-range dependency with linear complexity.
Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes
Can we humans correctly perceive the authenticity of the content of the videos we watch?
PANet: A Physics-guided Parametric Augmentation Net for Image Dehazing by Hazing
Image dehazing faces challenges when dealing with hazy images in real-world scenarios.
A self-supervised CNN for image watermark removal
Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal.
Beyond Night Visibility: Adaptive Multi-Scale Fusion of Infrared and Visible Images
Thereby, we obtain an image free of low light and light effects, which improves the performance of nighttime object detection.
A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution
The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution.
ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration
Besides, ViStripformer is an effective and efficient transformer architecture with much lower memory usage than the vanilla transformer.
ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation
Since continuous motion causes blurred artifacts during image exposure, we aspire to develop a groundbreaking blur augmentation method to generate diverse blurred images by simulating motion trajectories in a continuous space.
Ranked #2 on
Deblurring
on RealBlur-J
AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection
For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset.
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
We also discuss notable benchmarks commonly utilized in the field.
TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting
For precise utilization of the reference features for guidance, a reference-patch alignment (Ref-PA) module is proposed to align the patch features of the reference and corrupted images and harmonize their style differences, while a reference-patch transformer (Ref-PT) module is proposed to refine the embedded reference feature.
Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration
Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection.
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation.
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation
Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks.
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs.
Meta Transferring for Deblurring
Most previous deblurring methods were built with a generic model trained on blurred images and their sharp counterparts.
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.
Magic ELF: Image Deraining Meets Association Learning and Transformer
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications.
Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion
Specifically, based on the two discoveries of local spatial similarity and adjacent temporal correspondence of the sequential image data, we propose a novel Target-Domain driven pseudo label Diffusion (TDo-Dif) scheme.
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).
Physics-guided Terahertz Computational Imaging
Visualizing information inside objects is an ever-lasting need to bridge the world from physics, chemistry, biology to computation.
Generative Adversarial Networks for Image Super-Resolution: A Survey
Second, we present popular architectures for GANs in big and small samples for image applications.
Stripformer: Strip Transformer for Fast Image Deblurring
Images taken in dynamic scenes may contain unwanted motion blur, which significantly degrades visual quality.
Ranked #8 on
Deblurring
on RealBlur-R
Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters
Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters.
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning
To address the problem, we propose a deep learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pre-trained pre-simulation model.
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding
Unlike previous methods that mainly focus on closing the domain gap caused by fog -- defogging the foggy images or fogging the clear images, we propose to alleviate the domain gap by considering fog influence and style variation simultaneously.
Ranked #4 on
Domain Adaptation
on Cityscapes-to-FoggyZurich
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment
We prove that, after partitioning $\mathcal{G}$ into $Q$ sub-graphs $\{\mathcal{G}^q\}^Q_{q=1}$, the smallest Gershgorin circle theorem (GCT) lower bound of $Q$ corresponding coefficient matrices -- $\min_q \lambda^-_{\min}(\mathbf{B}^q)$ -- is a lower bound for $\lambda_{\min}(\mathbf{B})$.
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.
Carrying out CNN Channel Pruning in a White Box
Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner w. r. t.
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.
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps.
Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction
Here we focus on the problem that noisy labels are primarily mislabeled samples, which tend to be concentrated near decision boundaries, rather than uniformly distributed, and whose features should be equivocal.
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face.
SiMaN: Sign-to-Magnitude Network Binarization
In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise.
BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring
Image motion blur results from a combination of object motions and camera shakes, and such blurring effect is generally directional and non-uniform.
Ranked #10 on
Deblurring
on RealBlur-R
Dual-Level Collaborative Transformer for Image Captioning
Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning.
High Quality Disparity Remapping With Two-Stage Warping
In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure.
Vocal Bursts Intensity Prediction
Vocal Bursts Valence Prediction
Image Inpainting Guided by Coherence Priors of Semantics and Textures
In this paper, we introduce coherence priors between the semantics and textures which make it possible to concentrate on completing separate textures in a semantic-wise manner.
Forgery Blind Inspection for Detecting Manipulations of Gel Electrophoresis Images
Recently, falsified images have been found in papers involved in research misconducts.
Rotated Binary Neural Network
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
Geometric data acquired from real-world scenes, e. g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc.
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks.
Ranked #18 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
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.
Ranked #55 on
Image Super-Resolution
on Set14 - 4x upscaling
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.
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge.
DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition
With their aid, DotFAN can learn a disentangled face representation and effectively generate face images of various facial attributes while preserving the identity of augmented faces.
From IC Layout to Die Photo: A CNN-Based Data-Driven Approach
By learning the shape correspondences between pairs of layout design patterns and their scanning electron microscope (SEM) images of the product wafer thereof, given an IC layout pattern, LithoNet can mimic the fabrication process to predict its fabricated circuit shape.
Deep Learning on Image Denoising: An overview
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.
A Real-time Global Inference Network for One-stage Referring Expression Comprehension
Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description.
Semantic-aware Image Deblurring
Specially, we propose a novel Structured-Spatial Semantic Embedding model for image deblurring (termed S3E-Deblur), which introduces a novel Structured-Spatial Semantic tree model (S3-tree) to bridge two basic tasks in computer vision: image deblurring (ImD) and image captioning (ImC).
Information Competing Process for Learning Diversified Representations
Learning representations with diversified information remains as an open problem.
DotSCN: Group Re-identification via Domain-Transferred Single and Couple Representation Learning
To gain the superiority of deep learning models, we treat a group as multiple persons and transfer the domain of a labeled ReID dataset to a G-ReID target dataset style to learn single representations.
How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style
SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to.
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable.
Depth-Aware Stereo Video Retargeting
As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics.
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data
Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem.
Graph Fourier Transform with Negative Edges for Depth Image Coding
Recent advent in graph signal processing (GSP) has led to the development of new graph-based transforms and wavelets for image / video coding, where the underlying graph describes inter-pixel correlations.
Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights
In response, we derive an optimal perturbation matrix $\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia additivity formula---so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive semi-definite, resulting in a stable signal prior.
