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Found 94 papers, 24 papers with code
Binarized Neural Network for Single Image Super Resolution
Lighter model and faster inference are the focus of current single image super-resolution (SISR) research.
Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls
In recent years, the problem of fuzzy clustering has been widely concerned.
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball
We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST.
Few-shot Font Generation by Learning Style Difference and Similarity
To address this issue, we propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font).
Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues
Face forgery detection plays an important role in personal privacy and social security.
All-to-key Attention for Arbitrary Style Transfer
Our answer is a novel all-to-key attention mechanism: each position of content features is matched to key positions of style features.
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard.
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours'.
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization Feature Space
We show that the cycle-consistency regularization helps to minimize the volume of the transition matrix T indirectly without exploiting the estimated noisy class posterior, which could further encourage the estimated transition matrix T to converge to its optimal solution.
Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network
In this paper, we propose a MLP-Mixer based multi-view multi-label neural network for network traffic classification.
Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images
By stacking various layers of CSA blocks, we propose the Fourier Complex Transformer (FCT) model to learn global contextual information from VHR aerial images following the hierarchical manners.
Granular-Ball Fuzzy Set and Its Implementation in SVM
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing.
FedForgery: Generalized Face Forgery Detection with Residual Federated Learning
Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery.
GBSVM: Granular-ball Support Vector Machine
GBSVM (Granular-ball Support Vector Machine) is an important attempt to use the coarse granularity of a granular-ball as the input to construct a classifier instead of a data point.
Visual Information Hiding Based on Obfuscating Adversarial Perturbations
Growing leakage and misuse of visual information raise security and privacy concerns, which promotes the development of information protection.
LKD-Net: Large Kernel Convolution Network for Single Image Dehazing
The designed DLKCB can split the deep-wise large kernel convolution into a smaller depth-wise convolution and a depth-wise dilated convolution without introducing massive parameters and computational overhead.
Improving Adversarial Robustness via Mutual Information Estimation
To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method.
Seeking Subjectivity in Visual Emotion Distribution Learning
In psychology, the \textit{Object-Appraisal-Emotion} model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory.
TransFA: Transformer-based Representation for Face Attribute Evaluation
The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning.
Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario
In recent years, with the rapid development of face editing and generation, more and more fake videos are circulating on social media, which has caused extreme public concerns.
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers.
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i. e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios.
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
In this paper, we learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition.
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images
Important findings on the use of spatial and spectral information in the autoencoder framework are discussed.
Semi-parametric Makeup Transfer via Semantic-aware Correspondence
The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer.
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
In addition, this paper first provides the mathematical models for the granular-ball covering.
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two.
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud
In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision.
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time.
Image-specific Convolutional Kernel Modulation for Single Image Super-resolution
Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples.
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network
Inspired by this, we propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images.
Self-supervised Contrastive Attributed Graph Clustering
Thus, for the OOS nodes, SCAGC can directly calculate their clustering labels.
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.
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id
However, since person Re-Id is an open-set problem, the clustering based methods often leave out lots of outlier instances or group the instances into the wrong clusters, thus they can not make full use of the training samples as a whole.
Single Image Dehazing with An Independent Detail-Recovery Network
In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.
Stimuli-Aware Visual Emotion Analysis
Then, we design three specific networks, i. e., Global-Net, Semantic-Net and Expression-Net, to extract distinct emotional features from different stimuli simultaneously.
Support-Set Based Cross-Supervision for Video Grounding
The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.
Effective and Efficient Graph Learning for Multi-view Clustering
Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks.
Multiple Graph Learning for Scalable Multi-view Clustering
Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer.
A Circular-Structured Representation for Visual Emotion Distribution Learning
Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks.
Learning the Non-Differentiable Optimization for Blind Super-Resolution
Instead of considering iterative strategy, we make the blur kernel predictor trainable in the whole blind SR model, in which AMNet is well-trained.
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
However, pre-processing methods may suffer from the robustness degradation effect, in which the defense reduces rather than improving the adversarial robustness of a target model in a white-box setting.
Towards Defending against Adversarial Examples via Attack-Invariant Features
However, given the continuously evolving attacks, models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples.
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.
Removing Adversarial Noise in Class Activation Feature Space
Then, we train a denoising model to minimize the distances between the adversarial examples and the natural examples in the class activation feature space.
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution
Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable degradation priors.
Syncretic Modality Collaborative Learning for Visible Infrared Person Re-Identification
Visible infrared person re-identification (VI-REID) aims to match pedestrian images between the daytime visible and nighttime infrared camera views.
ADD-Defense: Towards Defending Widespread Adversarial Examples via Perturbation-Invariant Representation
Motivated by this observation, we propose a defense framework ADD-Defense, which extracts the invariant information called \textit{perturbation-invariant representation} (PIR) to defend against widespread adversarial examples.
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Based on the theorem, we propose the LRA framework for accelerating rough set algorithms.
Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution
To solve them, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose on improving the detail fidelity, instead of blindly designing or employing the deep CNNs architectures for merely feature representation in local receptive fields.
Robust Person Re-Identification through Contextual Mutual Boosting
The mask recalibrates the features to amplify the valuable characteristics and diminish the noise.
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.
Tasks Integrated Networks: Joint Detection and Retrieval for Image Search
The traditional object retrieval task aims to learn a discriminative feature representation with intra-similarity and inter-dissimilarity, which supposes that the objects in an image are manually or automatically pre-cropped exactly.
Weakly Supervised Temporal Action Localization with Segment-Level Labels
Specifically, we devise a partial segment loss regarded as a loss sampling to learn integral action parts from labeled segments.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction
Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks.
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.
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.
Facial Attribute Capsules for Noise Face Super Resolution
In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs.
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames.
Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking
To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm, which asynchronously and effectively warps the spatio-temporal information of asynchronous retinal events to a sequence of ATSLTD frames with clear object contours.
Image Fine-grained Inpainting
Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones.
Ranked #1 on
Facial Inpainting
on FFHQ
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.
Lightweight Image Super-Resolution with Information Multi-distillation Network
In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results.
Ranked #9 on
Image Super-Resolution
on Manga109 - 3x upscaling
Progressive Perception-Oriented Network for Single Image Super-Resolution
In this paper, we propose a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network.
Ranked #6 on
Image Super-Resolution
on Manga109 - 4x upscaling
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
In addition, we introduce a novel three-stage learning approach which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process.
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
3D image segmentation is one of the most important and ubiquitous problems in medical image processing.
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge.
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention.
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval
Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image.
Transfer Adaptation Learning: A Decade Survey
Domain is referred to as the state of the world at a certain moment.
A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction
The former aims to mimic the functions of peripheral vision to encode the holistic information and provide the attended regions.
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution
To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features.
Saliency deep embedding for aurora image search
In this paper, we aim to explore a new search method for images captured with circular fisheye lens, especially the aurora images.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations.
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
Recently, deep convolutional neural networks (CNNs) have been demonstrated remarkable progress on single image super-resolution.
Ranked #4 on
Image Super-Resolution
on IXI
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network
To improve information flow and to capture sufficient knowledge for reconstructing the high-frequency details, we propose a cascaded multi-scale cross network (CMSC) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner.
Restricting Greed in Training of Generative Adversarial Network
Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning.
Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding
We address the problem of dense visual-semantic embedding that maps not only full sentences and whole images but also phrases within sentences and salient regions within images into a multimodal embedding space.
Random Sampling for Fast Face Sketch Synthesis
The most time-consuming or main computation complexity for exemplar-based face sketch synthesis methods lies in the neighbor selection process.
Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors.
Ordinal Regression With Multiple Output CNN for Age Estimation
To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem.
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics.
Graphical Representation for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i. e., different sensors or different wavelengths) for identification.
Facial Feature Point Detection: A Comprehensive Survey
CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point.
Semi-supervised Relational Topic Model for Weakly Annotated Image Recognition in Social Media
In such way, we can efficiently leverage the loosely related tags, and build an intermediate level representation for a collection of weakly annotated images.
Learning to Rank for Blind Image Quality Assessment
The proposed BIQA method is one of learning to rank.
