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Found 72 papers, 32 papers with code
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning
Inspired by this, we propose a novel A-O disentangled framework for CZSL, namely Class-specified Cascaded Network (CSCNet).
Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks
Given a pair of captured images, subtle perturbations and distortions which go unnoticed by the human visual system tend to attack the correspondence matching, impairing the performance of image stitching algorithms.
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks.
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications.
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments
Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention.
Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation
In this work, we try to arouse the potential of enhancer + detector.
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
To overcome these limitations, we propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
Learning Heavily-Degraded Prior for Underwater Object Detection
Therefore, we propose a residual feature transference module (RFTM) to learn a mapping between deep representations of the heavily degraded patches of DFUI- and underwater- images, and make the mapping as a heavily degraded prior (HDP) for underwater detection.
Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration
Both stages directly estimate the respective target deformation fields.
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations.
Ranked #16 on
Thermal Image Segmentation
on MFN Dataset
Bilevel Generative Learning for Low-Light Vision
In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain.
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation.
Ranked #3 on
Semantic Segmentation
on FMB Dataset
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond
Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications.
Multi-Spectral Image Stitching via Spatial Graph Reasoning
Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene.
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications
The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms.
Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios
Based on these factual opinions, we propose a bilevel optimization formulation for jointly learning underwater object detection and image enhancement, and then unroll to a dual perception network (DPNet) for the two tasks.
Bilevel Fast Scene Adaptation for Low-Light Image Enhancement
A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes (i. e., freezing the encoder in the adaptation and testing phases).
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining
Extensive experiments on synthetic benchmarks and real-world images demonstrate that the proposed RDMC delivers strong performance on the depiction of rain streaks and outperforms the state-of-the-art methods.
A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion
Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e. g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation
In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of segmentation models against natural degradation factors (e. g., rain streaks) or artificially attack factors (e. g., adversarial attack).
Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion
In recent years, deep learning-based methods have achieved remarkable progress in multi-exposure image fusion.
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes
3D object detection plays a crucial role in numerous intelligent vision systems.
An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection
Their common characteristic of seeking complementary cues from different source images motivates us to explore the collaborative relationship between Fusion and Salient object detection tasks on infrared and visible images via an Interactively Reinforced multi-task paradigm for the first time, termed IRFS.
NAI$_2$: Learning Noise-Aware Illuminance-Interpolator for Unsupervised Low-Light Image Enhancement
Low-light situations severely restrict the pursuit of aesthetic quality in consumer photography.
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
Recently, multi-modality scene perception tasks, e. g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems.
Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration
Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception.
Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection
A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features.
Practical Exposure Correction: Great Truths Are Always Simple
Improving the visual quality of the given degraded observation by correcting exposure level is a fundamental task in the computer vision community.
Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion
To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules.
CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion
Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors.
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement
To address the above limitations, we develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model, aiming to exploit its hierarchical feature representation as an auxiliary for the low-level underwater image enhancement.
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm.
CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data
data issues and Byzantine attacks, global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence.
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution
As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years.
Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration
Moreover, to better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM) to adaptively select more meaningful features for fusion in the Dual-path Interaction Fusion Network (DIFN).
Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application
In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs).
Learning Weighting Map for Bit-Depth Expansion within a Rational Range
Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth.
Toward Fast, Flexible, and Robust Low-Light Image Enhancement
Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios.
Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection
This study addresses the issue of fusing infrared and visible images that appear differently for object detection.
Ranked #1 on
Object Detection
on Multispectral Dataset
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions
Deformable image registration plays a critical role in various tasks of medical image analysis.
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details.
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way
To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion.
Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision
To partially address above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, which not only can address low-light enhancement task, but also has the flexibility to handle other more challenging downstream vision applications.
Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement
Further, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short).
Triple-level Model Inferred Collaborative Network Architecture for Video Deraining
In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances.
BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers
As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm.
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset
Based on this dataset, we propose a semi-supervised underwater semantic segmentation network focusing on the boundaries(US-Net: Underwater Segmentation Network).
Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching
Generating high-quality stitched images with natural structures is a challenging task in computer vision.
A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
Towards these challenges we introduce a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark, based on the collection and re-annotation of all relevant datasets.
Joint Optimization of Communications and Federated Learning Over the Air
Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy.
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog aggregation transmissions.
Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
Low-light image enhancement plays very important roles in low-level vision field.
Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision
Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i. e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations.
Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
We design a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation in order to integrate advantages and avoid limitations of these two categories of approaches.
A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing
We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively.
Converged Deep Framework Assembling Principled Modules for CS-MRI
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR data acquisition at a sampling rate much lower than the Nyquist criterion.
Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling
Properly modeling latent image distributions plays an important role in a variety of image-related vision problems.
Position Focused Attention Network for Image-Text Matching
Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation
Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments.
Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving
To this end, we first leverage a stand-alone module to transform the input data from 2D image plane to 3D point clouds space for a better input representation, then we perform the 3D detection using PointNet backbone net to obtain objects 3D locations, dimensions and orientations.
Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years.
A Bridging Framework for Model Optimization and Deep Propagation
Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios.
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications.
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague.
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond
Blind image deblurring plays a very important role in many vision and multimedia applications.
Toward Designing Convergent Deep Operator Splitting Methods for Task-specific Nonconvex Optimization
Operator splitting methods have been successfully used in computational sciences, statistics, learning and vision areas to reduce complex problems into a series of simpler subproblems.
Self-Reinforced Cascaded Regression for Face Alignment
Cascaded regression is prevailing in face alignment thanks to its accuracy and robustness, but typically demands manually annotated examples having low discrepancy between shape-indexed features and shape updates.
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Deep learning models have gained great success in many real-world applications.
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
However, they may fail when their assumptions are not valid on specific images.
Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
We develop dual deep networks with memorable gated recurrent units (GRUs), and sequentially feed these two types of features into the dual networks, respectively.
Mining Functional Modules by Multiview-NMF of Phenome-Genome Association
In particular, the hierarchical structure of ontology has not been sufficiently utilized in clustering genes while functionally related genes are consistently associated with phenotypes on the same path in the phenotype ontology.
A Fast Ellipse Detector Using Projective Invariant Pruning
Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue.
