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Found 15 papers, 4 papers with code
A Multi-modal Intelligent Channel Model for 6G Multi-UAV-to-Multi-Vehicle Communications
In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-unmanned aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed.
Synesthesia of Machines Based Multi-Modal Intelligent V2V Channel Model
In the developed ScaR algorithm, the mapping relationship between LiDAR point clouds and scatterers is explored, where the distribution of scatterers is obtained in the form of grid maps.
SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)
Given the importance of datasets for sensing-communication integration research, a novel simulation platform for constructing communication and multi-modal sensory dataset is developed.
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts
Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation.
Multi-Modal Intelligent Channel Modeling: A New Modeling Paradigm via Synesthesia of Machines
In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel modeling are indispensable.
From 2015 to 2023: How Machine Learning Aids Natural Product Analysis
In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors.
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis.
Scatterer Recognition for Multi-Modal Intelligent Vehicular Channel Modeling via Synesthesia of Machines
In this paper, a novel multi-modal intelligent vehicular channel model is proposed by scatterer recognition from light detection and ranging (LiDAR) point clouds via Synesthesia of Machines (SoM).
Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs.
A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration
The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time.
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models
Counterfactual reasoning is often used in clinical settings to explain decisions or weigh alternatives.
Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines
Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review.
M$^3$SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration
A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research.
DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning
The task of grasp pattern recognition aims to derive the applicable grasp types of an object according to the visual information.
Estimating smooth and sparse neural receptive fields with a flexible spline basis
Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron.
