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Found 97 papers, 41 papers with code
Beam Domain Channel Estimation for Spatial Non-Stationary Massive MIMO Systems
In massive multiple-input multiple-output (MIMO) systems, the channel estimation scheme is subject to the spatial non-stationarity and inevitably power leakage in the beam domain.
A Novel Non-Stationary Channel Emulator for 6G MIMO Wireless Channels
The performance evaluation of sixth generation (6G) communication systems is anticipated to be a controlled and repeatable process in the lab, which brings up the demand for wireless channel emulators.
Wireless Channel Measurements and Characterization in Industrial IoT Scenarios
For SISO channel measurements, statistical properties including the delay Power Spectral Density (PSD), path loss, shadowing fading, delay spread, excess delay, K-factor, and amplitude distribution of small-scale fading are analyzed and compared with those observed in an office scenario.
A 3D Continuous-Space Electromagnetic Channel Model for 6G Tri-Polarized Multi-user Communications
Simulation results demonstrate that transmit power, apertures, scatterers, and sample intervals have significant impacts on statistical properties and channel capacities, providing insights into the performance of continuous-space electromagnetic channel models and the design of future wireless systems.
An Efficient Pre-Processing Method for 6G Dynamic Ray-Tracing Channel Modeling
The ray-tracing is often employed in urban areas for channel modeling with high accuracy but encounters a substantial computational complexity for high mobility scenarios.
Ray-Tracing Channel Modeling for LEO Satellite-to-Ground Communication Systems
Based on the vision of global coverage for sixth-generation (6G) wireless communication systems, the low earth orbit (LEO) satellite-to-ground channel model for urban scenarios has emerged as highly important for the system design.
RefHCM: A Unified Model for Referring Perceptions in Human-Centric Scenarios
Human-centric perceptions play a crucial role in real-world applications.
Multimodal Sentiment Analysis Based on Causal Reasoning
However, multimodal sentiment analysis is affected by unimodal data bias, e. g., text sentiment is misleading due to explicit sentiment semantic, leading to low accuracy in the final sentiment classification.
Take Package as Language: Anomaly Detection Using Transformer
Network data packet anomaly detection faces numerous challenges, including exploring new anomaly supervision signals, researching weakly supervised anomaly detection, and improving model interpretability.
SimulBench: Evaluating Language Models with Creative Simulation Tasks
To facilitate automatic assessment on \DataName{}, GPT-4 is employed as the evaluator, tasked with reviewing the quality of the final response generated by the target LLMs given multi-turn dialogue scripts.
Segmentation by registration-enabled SAM prompt engineering using five reference images
This approach utilises established image registration algorithms to align the new image (to-be-segmented) and a small number of reference images, without requiring segmentation labels.
Infrared Image Super-Resolution via Lightweight Information Split Network
A key innovation within this model is the introduction of the Lightweight Information Split Block (LISB) for deep feature extraction.
Long-form factuality in large language models
Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators - on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time.
Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
This iterative cycle of exploration and training fosters continued improvement in the agents.
Revisiting Spatial-Frequency Information Integration from a Hierarchical Perspective for Panchromatic and Multi-Spectral Image Fusion
Our method extend a new space for exploring the relationships of PAN and LRMS images enhancing the integration of spatial-frequency information.
Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion
To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility.
Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance
At the second stage the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images.
HomoFormer: Homogenized Transformer for Image Shadow Removal
The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models which may lead to an unsatisfactory compromise.
Ranked #3 on
Shadow Removal
on ISTD+
Cascade Speculative Drafting for Even Faster LLM Inference
Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft.
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration
The former facilitates channel-dependent degradation removal operation, allowing the network to tailor responses to various adverse weather types; the latter, by integrating Fourier's global properties into channel-independent content features, enhances network capacity for consistent global content reconstruction.
A WINNER+ Based 3-D Non-Stationary Wideband MIMO Channel Model
In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed.
A Novel 3D Non-stationary Localization-assisted ISAC Channel Model
Integrated sensing and communication (ISAC) has attracted wide attention as an emerging application scenario for the sixth generation (6G) wireless communication system.
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling
Besides, we provide a simple solution extending a neural topic model to reduce the effect of noisy out-of-topics text generation from PLMs.
Text Fact Transfer
Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content.
Ask To The Point: Open-Domain Entity-Centric Question Generation
We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking.
Neural Degradation Representation Learning for All-In-One Image Restoration
To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations.
Descriptive Knowledge Graph in Biomedical Domain
We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge.
Configuration Validation with Large Language Models
We present a first analysis on the feasibility and effectiveness of using LLMs for configuration validation.
Unmasking Bias in Diffusion Model Training
In this paper, we identify that these obstacles can be largely attributed to bias and suboptimality inherent in the default training paradigm of diffusion models.
Large Language Models Cannot Self-Correct Reasoning Yet
Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications.
Empowering Low-Light Image Enhancer through Customized Learnable Priors
In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm.
Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusion
Such designs penetrate the image reasoning prior into deep unfolding networks while improving its interpretability and representation capability.
Bearing-based Formation with Disturbance Rejection
For the case of the known frequencies, we employ the canonical internal model to solve the problem, and, for the case of the unknown frequencies, we combine the canonical internal model and {some} distributed adaptive control technique to deal with the problem.
Situated Natural Language Explanations
Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
Generalized Lightness Adaptation with Channel Selective Normalization
Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability.
High-quality Image Dehazing with Diffusion Model
The latter stage exploits the strong generation ability of DDPM to compensate for the haze-induced huge information loss, by working in conjunction with the physical modelling.
RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models.
Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches
Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers.
Citation: A Key to Building Responsible and Accountable Large Language Models
Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development.
Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation
When answering complex questions, large language models (LLMs) may produce answers that do not satisfy all criteria of the question.
Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage
The advancement of large language models (LLMs) brings notable improvements across various applications, while simultaneously raising concerns about potential private data exposure.
CCGen: Explainable Complementary Concept Generation in E-Commerce
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e. g., "Digital Cameras", generating a list of complementary concepts, e. g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers.
Expository Text Generation: Imitate, Retrieve, Paraphrase
Expository documents are vital resources for conveying complex information to readers.
Why Does ChatGPT Fall Short in Providing Truthful Answers?
To better understand the model's particular weaknesses in providing truthful answers, we embark an in-depth exploration of open-domain question answering.
Multi-step Jailbreaking Privacy Attacks on ChatGPT
With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts.
Region-Aware Portrait Retouching with Sparse Interactive Guidance
Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch.
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
We investigate the efficacy of our belief from three perspectives: 1) from task-customized MAE to native MAE, 2) from image task to video task, and 3) from transformer structure to convolution neural network structure.
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''.
Exploring Temporal Frequency Spectrum in Deep Video Deblurring
Video deblurring aims to restore the latent video frames from their blurred counterparts.
Learning Sample Relationship for Exposure Correction
Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network.
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
Ingredient-Oriented Multi-Degradation Learning for Image Restoration
Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
Towards Reasoning in Large Language Models: A Survey
Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking.
DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
MoREE consists of a mixture of retrievers model that retrieves diverse context sentences related to the given concepts, and a mixture of generators model that generates diverse sentences based on the retrieved contexts.
VER: Unifying Verbalizing Entities and Relations
To know the relationship between two entities, humans tend to create a sentence to connect them.
MetaASSIST: Robust Dialogue State Tracking with Meta Learning
Experimental results demonstrate that all three schemes can achieve competitive performance.
Coordinated Topic Modeling
It then uses the axes to model a corpus for easily understandable representation.
Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network
Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to generate the expected high-resolution (HR) MS images, conditioning on the corresponding high-resolution PAN images.
Can Language Models Be Specific? How?
We hope this work can bring to awareness the notion of specificity of language models and encourage the research community to further explore this important but understudied problem.
Deep Fourier Up-Sampling
Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling.
CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal
The key to shadow removal is recovering the contents of the shadow regions with the guidance of the non-shadow regions.
An Improved Equiangular Division Algorithm for SBR based Ray Tracing Channel Modeling
With the proposed iterative precise algorithm, error of angle of departure (AOD) and angle of arrival (AOA) is below 0. 01 degree.
An SBR Based Ray Tracing Channel Modeling Method for THz and Massive MIMO Communications
Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology have been proved significant for the sixth generation (6G) communication systems, and have gained global interests.
A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications
Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios.
An Improved Ray Tracing Acceleration Algorithm Based on Bounding Volume Hierarchies
Ray tracing is an efficient channel modeling method.
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic.
Source-Free Domain Adaptation for Real-world Image Dehazing
Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains.
Reconfigurable intelligent surfaces: Channel characterization and modeling
Reconfigurable intelligent surfaces (RISs) are two dimensional (2D) metasurfaces which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements.
Are Large Pre-Trained Language Models Leaking Your Personal Information?
Are Large Pre-Trained Language Models Leaking Your Personal Information?
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships.
An unsupervised approach for semantic place annotation of trajectories based on the prior probability
The Bayesian Criterion is specifically employed to decompose the spatiotemporal probability of the candidate place into spatial probability, duration probability, and visiting time probability.
A Novel 3D Non-Stationary Channel Model for 6G Indoor Visible Light Communication Systems
The visible light communication (VLC) technology has attracted much attention in the research of the sixth generation (6G) communication systems.
Domain Representative Keywords Selection: A Probabilistic Approach
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain.
CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors
In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e. g., optical or radar).
Exposure Normalization and Compensation for Multiple-Exposure Correction
However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.
Bijective Mapping Network for Shadow Removal
Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature.
Ranked #8 on
Shadow Removal
on ISTD+
Mutual Information-Driven Pan-Sharpening
Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information learning between two modalities of PAN and MS images.
Understanding Jargon: Combining Extraction and Generation for Definition Modeling
From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge.
Open Relation Modeling: Learning to Define Relations between Entities
Relations between entities can be represented by different instances, e. g., a sentence containing both entities or a fact in a Knowledge Graph (KG).
A Novel 3D Non-Stationary GBSM for 6G THz Ultra-Massive MIMO Wireless Systems
Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems.
Multi-Frequency Wireless Channel Measurements and Characteristics Analysis in Indoor Corridor Scenarios
In this paper, we conduct wireless channel measurements in indoor corridor scenarios at 2. 4, 5 and 6 GHz bands with bandwidth of 320 MHz.
Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm
It can simultaneously extract the forward and backward components of multiple bearing sections and realize non-stationary complex signal decomposition of multiple bearing sections of the rotor.
Space-Time Distillation for Video Super-Resolution
In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one.
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach
To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain.
EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
Image deblurring has seen a great improvement with the development of deep neural networks.
A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surfaces with Practical Phase Shifts
In this paper, a three-dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing practical discrete intelligent reflecting surface (IRS) is proposed.
A Multi-Size Neural Network with Attention Mechanism for Answer Selection
The experimental results show that (1) multi-size neural network (MSNN) is a more useful method to capture abstract features on different levels of granularities than single/multi-layer CNNs; (2) the attention mechanism (AM) is a better strategy to derive more informative representations; (3) AM-MSNN is a better architecture for the answer selection task for the moment.
A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems
The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users.
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback
Deep reinforcement learning (DRL) has achieved great successes in many simulated tasks.
A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface
The evolution of clusters on the linear array and planar array is also considered in the proposed model.
6G Oriented Wireless Communication Channel Characteristics Analysis and Modeling
Based on the vision on the 6G wireless communication network, i. e., global coverage, all spectrums and all applications, we comprehensively survey 6G related wireless channel measurements, channel characteristics, and channel models for all frequency bands and all scenarios.
Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems
Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G).
A Big Data Enabled Channel Model for 5G Wireless Communication Systems
This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling.
Hyper-Path-Based Representation Learning for Hyper-Networks
Then a carefully designed algorithm, Hyper-gram, utilizes these random walks to capture both pairwise relationships and tuplewise relationships in the whole hyper-networks.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Video-based Sign Language Recognition without Temporal Segmentation
Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data.
