Search Results for author:
Found 119 papers, 45 papers with code
End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention
Coded aperture snapshot spectral imaging (CASSI) is an effective tool to capture real-world 3D hyperspectral images.
Ranked #7 on
Spectral Reconstruction
on Real HSI
BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging
This measurement and the modulation masks are fed into our Recurrent Neural Network (RNN) to reconstruct the desired high-speed frames.
Deep Optics for Video Snapshot Compressive Imaging
We believe this is a milestone for real-world video SCI.
Dual-Scale Transformer for Large-Scale Single-Pixel Imaging
In this paper, we propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model, dubbed HATNet, to improve the imaging quality of real SPI cameras.
Binarized Low-light Raw Video Enhancement
For the second issue, we present a distribution-aware binary convolution, which captures the distribution characteristics of real-valued input and incorporates them into plain binary convolutions to alleviate the degradation in performance.
SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image
SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors.
A First Look at GPT Apps: Landscape and Vulnerability
With the advancement of Large Language Models (LLMs), increasingly sophisticated and powerful GPTs are entering the market.
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability.
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation.
SnapCap: Efficient Snapshot Compressive Video Captioning
To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap.
Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence
The model segmentation without user mobility has been investigated deeply by previous works.
Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB.
High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing
Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink).
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones.
OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface
Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence.
Latent Diffusion Prior Enhanced Deep Unfolding for Spectral Image Reconstruction
Snapshot compressive spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement.
Binarized 3D Whole-body Human Mesh Recovery
In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.
Image Super-Resolution with Text Prompt Diffusion
Specifically, we first design a text-image generation pipeline to integrate text into the SR dataset through the text degradation representation and degradation model.
Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation
Mis- and disinformation online have become a major societal problem as major sources of online harms of different kinds.
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation
We develop a neural network architecture which, trained in an unsupervised manner as a denoising diffusion model, simultaneously learns to both generate and segment images.
Learning to Recover for Safe Reinforcement Learning
Safety controllers is widely used to achieve safe reinforcement learning.
Mobile Foundation Model as Firmware
Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.
Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models
In this paper, we study the denoising diffusion probabilistic model (DDPM) in wavelet space, instead of pixel space, for visual synthesis.
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments.
Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging
We consider the problem of video snapshot compressive imaging (SCI), where sequential high-speed frames are modulated by different masks and captured by a single measurement.
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method.
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process.
Binarized Spectral Compressive Imaging
Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model.
EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging
We are the first time to show that an UHD color video with high compression ratio can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 32 dB.
Learn to Unlearn: A Survey on Machine Unlearning
This inspired recent research on removing the influence of specific data samples from a trained ML model.
New Adversarial Image Detection Based on Sentiment Analysis
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e. g., DeepFool, are on the rise and outrunning adversarial example detection techniques.
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models.
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework
This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications.
Universal Domain Adaptation for Remote Sensing Image Scene Classification
Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not.
Large-scale Global Low-rank Optimization for Computational Compressed Imaging
However, the computational cost has inhibited NLR from seeking global structural similarity, which consequentially keeps it trapped in the tradeoff between accuracy and efficiency and prevents it from high-dimensional large-scale tasks.
Cross Aggregation Transformer for Image Restoration
The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.
Learning Visual Representation of Underwater Acoustic Imagery Using Transformer-Based Style Transfer Method
Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering.
Accurate Image Restoration with Attention Retractable Transformer
This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions.
S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction
By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction.
Spatial-Temporal Transformer for Video Snapshot Compressive Imaging
In this paper, we consider the reconstruction algorithm in video SCI, i. e., recovering a series of video frames from a compressed measurement.
Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models
In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective.
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork
We develop an approach for text-to-image generation that embraces additional retrieval images, driven by a combination of implicit visual guidance loss and generative objectives.
Trajectory Planning of Cellular-Connected UAV for Communication-assisted Radar Sensing
Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness.
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement.
Ranked #1 on
Spectral Reconstruction
on Real HSI
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
We consider the image and video compression on resource limited platforms.
AdaVocoder: Adaptive Vocoder for Custom Voice
The solution to constructing a custom voice is to combine an adaptive acoustic model with a robust vocoder.
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i. e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement.
Ranked #2 on
Spectral Reconstruction
on Real HSI
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features.
Ranked #5 on
Spectral Reconstruction
on Real HSI
Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing
Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compresses them into a single measurement.
Ensemble learning priors unfolding for scalable Snapshot Compressive Sensing
To address these problems, we develop the ensemble learning priors to further improve the reconstruction accuracy and propose the scalable learning to empower deep learning the scalability just like the traditional algorithm.
Deep Equilibrium Models for Video Snapshot Compressive Imaging
The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data has led to an inverse problem, which consists of recovering the HD signal from the compressed and noisy measurement.
Spectral Compressive Imaging Reconstruction Using Convolution and Contextual Transformer
Spectral compressive imaging (SCI) is able to encode the high-dimensional hyperspectral image to a 2D measurement, and then uses algorithms to reconstruct the spatio-spectral data-cube.
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing
We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure.
Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging
Towards this end, in this work, we propose the online PnP algorithm which can adaptively update the network's parameters within the PnP iteration; this makes the denoising network more applicable to the desired data in the SCI reconstruction.
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction
Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system.
Nonlinear Intensity Underwater Sonar Image Matching Method Based on Phase Information and Deep Convolution Features
In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.
Nonlinear Intensity Sonar Image Matching based on Deep Convolution Features
In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
The HSI representations are highly similar and correlated across the spectral dimension.
Ranked #2 on
Spectral Reconstruction
on ARAD-1K
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal.
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision.
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network
In this paper, we propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds.
Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging
In this paper, inspired by the untrained neural networks such as deep image priors (DIP) and deep decoders, we develop a framework by integrating DIP into the plug-and-play regime, leading to a self-supervised network for spectral SCI reconstruction.
Matching Underwater Sonar Images by the Learned Descriptor Based on Style Transfer Method
This paper proposes a method that combines the style transfer technique and the learned descriptor to enhance the matching performances of underwater sonar images.
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data
Subsea images measured by the side scan sonars (SSSs) are necessary visual data in the process of deep-sea exploration by using the autonomous underwater vehicles (AUVs).
A Matching Algorithm based on Image Attribute Transfer and Local Features for Underwater Acoustic and Optical Images
Additionally, the method is based on the combination of image depth semantic layer, and it could indirectly display the local feature matching relationship between original image pair, which provides a new solution to the underwater multi-sensor image matching problem.
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way.
10-mega pixel snapshot compressive imaging with a hybrid coded aperture
Towards this end, snapshot compressive imaging (SCI) was proposed as a promising solution to improve the throughput of imaging systems by compressive sampling and computational reconstruction.
Reinforcement Learning for Adaptive Video Compressive Sensing
We apply reinforcement learning to video compressive sensing to adapt the compression ratio.
Multimodal Contrastive Training for Visual Representation Learning
We first train our model on COCO and evaluate the learned visual representations on various downstream tasks including image classification, object detection, and instance segmentation.
Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution
High quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations.
Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging
Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).
Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications
Via novel optical designs, the 2D detector samples the HD data in a {\em compressive} manner; following this, algorithms are employed to reconstruct the desired HD data-cube.
Memory-Efficient Network for Large-scale Video Compressive Sensing
With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing
To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.
Plug-and-Play Algorithms for Video Snapshot Compressive Imaging
On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm.
Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging
Specifically, the RGB measurement is utilized to estimate the coefficients, meanwhile the CASSI measurement is adopted to provide the orthogonal spectral basis.
GAP-net for Snapshot Compressive Imaging
The hardware encoder typically consists of an (optical) imaging system designed to capture {compressed measurements}.
Multi-Path Routing on the Jellyfish Networks
The Jellyfish network has recently be proposed as an alternate to the fat-tree network as the interconnect for data centers and high performance computing clusters.
Networking and Internet Architecture Distributed, Parallel, and Cluster Computing
Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.
Performance Evaluation and Modeling of Cryptographic Libraries for MPI Communications
In order for High-Performance Computing (HPC) applications with data security requirements to execute in the public cloud, the cloud infrastructure must ensure the privacy and integrity of data.
Distributed, Parallel, and Cluster Computing Cryptography and Security
CryptMPI: A Fast Encrypted MPI Library
The cloud infrastructure must provide security for High-Performance Computing (HPC) applications of sensitive data to execute in such an environment.
Distributed, Parallel, and Cluster Computing Cryptography and Security
Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images
First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images.
Growing Efficient Deep Networks by Structured Continuous Sparsification
We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives.
The Power of Triply Complementary Priors for Image Compressive Sensing
In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.
Various Total Variation for Snapshot Video Compressive Imaging
Various TV denoising and projection algorithms are developed and tested for video SCI reconstruction on both simulation and real datasets.
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D) images using a 2D sensor (detector) in a single snapshot.
ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities
The augmented images help to tell apart the edge gradients, edge pixels and non-edge ones in log-histogram, which contribute significantly to the feature extraction and color-ambiguity elimination, thereby advancing the accuracy of illuminant estimation.
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme
Bearing these concerns in mind, we propose DRCAS (Deep Restoration network for hardware based Compressed Acquisition Scheme), which to our best knowledge, is the first work proposed in the literature for restoration of images acquired using acquisition scheme like HCAS.
Auto-encoders for compressed sensing
Compressed sensing is about recovering a structured high-dimensional signal ${\bf x}\in R^n$ from its under-determined noisy linear measurements ${\bf y}\in R^m$, where $m\ll n$.
Enhanced Bayesian Compression via Deep Reinforcement Learning
In this paper, we propose an Enhanced Bayesian Compression method to flexibly compress the deep networks via reinforcement learning.
Relaxation-Free Deep Hashing via Policy Gradient
In this paper, we propose a simple yet effective relaxation-free method to learn more effective binary codes via policy gradient for scalable image search.
Deep Reinforcement Learning with Iterative Shift for Visual Tracking
Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking.
Rank Minimization for Snapshot Compressive Imaging
We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction.
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image.
Deep Hashing via Discrepancy Minimization
This paper presents a discrepancy minimizing model to address the discrete optimization problem in hashing learning.
Group Sparsity Residual with Non-Local Samples for Image Denoising
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing.
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications.
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization
Sparse coding has achieved a great success in various image processing tasks.
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG
We present an end-to-end image compression system based on compressive sensing.
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM.
Block-wise Lensless Compressive Camera
2) The coding patterns used in each block can be the same, therefore the sensing matrix is only of the block size compared to the entire image size in existing $\text{L}^2\text{C}^2$.
Compressive Sensing via Convolutional Factor Analysis
During reconstruction and testing, we project the upper layer dictionary to the data level and only a single layer deconvolution is required.
Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as associated labels or captions.
A Deep Generative Deconvolutional Image Model
A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework.
Compressive Sensing via Low-Rank Gaussian Mixture Models
We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM).
Lensless Compressive Imaging
We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens.
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
A Bayesian model based on the ranks of the data is proposed.
A Generative Model for Deep Convolutional Learning
A generative model is developed for deep (multi-layered) convolutional dictionary learning.
Compressive Hyperspectral Imaging with Side Information
By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality.
Generative Deep Deconvolutional Learning
A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning.
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
A new Bayesian formulation is developed for nonlinear support vector machines (SVMs), based on a Gaussian process and with the SVM hinge loss expressed as a scaled mixture of normals.
Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information
These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of linear features extracted from the signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay.
Tree-Structure Bayesian Compressive Sensing for Video
A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system.
Low-Cost Compressive Sensing for Color Video and Depth
A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for each of the original measured frames, and each of the recovered frames can be focused at a different depth.
Multiscale Shrinkage and Lévy Processes
The method we consider in detail, and for which numerical results are presented, is based on increments of a gamma process.
Adaptive Temporal Compressive Sensing for Video
This paper introduces the concept of adaptive temporal compressive sensing (CS) for video.
