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Found 37 papers, 10 papers with code
Green Robotic Mixed Reality with Gaussian Splatting
Realizing green communication in robotic mixed reality (RoboMR) systems presents a challenge, due to the necessity of uploading high-resolution images at high frequencies through wireless channels.
TextInPlace: Indoor Visual Place Recognition in Repetitive Structures with Scene Text Spotting and Verification
Visual Place Recognition (VPR) is a crucial capability for long-term autonomous robots, enabling them to identify previously visited locations using visual information.
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering.
EmoAssist: Emotional Assistant for Visual Impairment Community
To address these gaps, this paper introduces the EmoAssist Benchmark, a comprehensive benchmark designed to evaluate the assistive performance of LMMs for the VI community.
Physics-informed deep learning for infectious disease forecasting
We also show that the performance of the PINN model is comparable to a sophisticated Gaussian infection state space with time dependence (GISST) forecasting model that integrates the compartment model with a data observation model and a regression model for inferring parameters in the compartment model.
A Geometric Analysis-Based Safety Assessment Framework for MASS Route Decision-Making in Restricted Waters
To enhance the safety of Maritime Autonomous Surface Ships (MASS) navigating in restricted waters, this paper aims to develop a geometric analysis-based route safety assessment (GARSA) framework, specifically designed for their route decision-making in irregularly shaped waterways.
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
To balance the trade-off between generalization and specialization, we propose measuring the parameter importance for both pre-trained and fine-tuning distributions, based on frozen pre-trained weight magnitude and accumulated fine-tuning gradient values.
Continuous Approximations for Improving Quantization Aware Training of LLMs
Model compression methods are used to reduce the computation and energy requirements for Large Language Models (LLMs).
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
CipherDM: Secure Three-Party Inference for Diffusion Model Sampling
Diffusion Models (DMs) achieve state-of-the-art synthesis results in image generation and have been applied to various fields.
Look Within, Why LLMs Hallucinate: A Causal Perspective
The emergence of large language models (LLMs) is a milestone in generative artificial intelligence, achieving significant success in text comprehension and generation tasks.
Autoregressive Image Generation without Vector Quantization
In this work, we propose to model the per-token probability distribution using a diffusion procedure, which allows us to apply autoregressive models in a continuous-valued space.
Ranked #14 on
Image Generation
on ImageNet 512x512
HORAE: A Domain-Agnostic Modeling Language for Automating Multimodal Service Regulation
Artificial intelligence is rapidly encroaching on the field of service regulation.
All in One Framework for Multimodal Re-identification in the Wild
In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks.
SparseDM: Toward Sparse Efficient Diffusion Models
Diffusion models represent a powerful family of generative models widely used for image and video generation.
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation
Widely available healthcare services are now getting popular because of advancements in wearable sensing techniques and mobile edge computing.
Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution
In vitro phantom results demonstrate that CCycleGAN successfully generates images with improved spatial resolution as well as higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with benchmarks.
When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA
Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving.
Rethinking Federated Learning With Domain Shift: A Prototype View
The private model presents degenerative performance on other domains (with domain shift).
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions.
Pyramidal Transformer with Conv-Patchify for Person Re-identification
The robust and discriminative feature extraction is the key component in person re-identification (Re-ID).
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance.
Random Ensemble Reinforcement Learning for Traffic Signal Control
Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of data reuses to improve the problem of low data utilization.
AnomMAN: Detect Anomaly on Multi-view Attributed Networks
It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks.
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs.
DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction
Detectors with high coverage have direct and far-reaching benefits for road users in route planning and avoiding traffic congestion, but utilizing these data presents unique challenges including: the dynamic temporal correlation, and the dynamic spatial correlation caused by changes in road conditions.
An Efficient Network Design for Face Video Super-resolution
We construct a dataset consisting entirely of face video sequences for network training and evaluation, and conduct hyper-parameter optimization in our experiments.
Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
In order to pursue faster VSR processing ability up to 4K resolution, this paper tries to choose lightweight network structure and efficient upsampling method to reduce the computation required by EGVSR network under the guarantee of high visual quality.
Ranked #2 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images
Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness.
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods
We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the distribution of search directions and the function-value query complexity.
Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis
To abandon this eigengap assumption, we consider a new route in our analysis: instead of exactly identifying the top-$L$-dim eigenspace, we show that our estimator is able to cover the targeted top-$L$-dim population eigenspace.
Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning.
Adversarial Examples: Attacks on Machine Learning-based Malware Visualization Detection Methods
As the threat of malicious software (malware) becomes urgently serious, automatic malware detection techniques have received increasing attention recently, where the machine learning (ML)-based visualization detection plays a significant role. However, this leads to a fundamental problem whether such detection methods can be robust enough against various potential attacks. Even though ML algorithms show superiority to conventional ones in malware detection in terms of high efficiency and accuracy, this paper demonstrates that such ML-based malware detection methods are vulnerable to adversarial examples (AE) attacks. We propose the first AE-based attack framework, named Adversarial Texture Malware Perturbation Attacks (ATMPA), based on the gradient descent or L-norm optimization method. By introducing tiny perturbations on the transformed dataset, ML-based malware detection methods completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the detection rate of convolutional neural network (CNN), support vector machine (SVM) and random forest(RF)-based malware detectors down to 0 and the attack transferability can achieve up to 88. 7% and 74. 1% on average in different ML-based detection methods.
Cryptography and Security
Robust Video Content Alignment and Compensation for Clear Vision Through the Rain
Extensive evaluations show that advantage of up to 5dB is achieved on the scene restoration PSNR over state-of-the-art methods, and the advantage is especially obvious with highly complex and dynamic scenes.
Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework
Visual inspection shows that much cleaner rain removal is achieved especially for highly dynamic scenes with heavy and opaque rainfall from a fast moving camera.
