ViT LoS V2X

ViT LoS V2X research focuses on leveraging Vision Transformers (ViTs) to predict line-of-sight (LoS) blockage in 6G vehicular networks, improving the reliability of high-frequency communication. Current efforts center on developing efficient ViT architectures, often incorporating convolutional layers or attention mechanisms optimized for resource-constrained environments and multimodal data fusion (e.g., combining camera images with beam vectors). This work is significant because accurate LoS blockage prediction is crucial for safe and efficient autonomous driving and the broader deployment of 6G vehicular communication technologies.

Papers

October 10, 2024

L-VITeX: Light-weight Visual Intuition for Terrain Exploration
Antar Mazumder, Zarin Anjum Madhiha
Resource Constrained Human Intuition Small Scale Robot Exploration Efficiency ViT LoS V2X

August 7, 2024

CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Tianfang Zhang, Lei Li, Yang Zhou, Wentao Liu, Chen Qian, Xiangyang Ji
Vision Transformer Self Attention Token Mixer ViT LoS V2X Token Mixing Mobile Vision

June 27, 2024

ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks
Ghazi Gharsallah, Georges Kaddoum
Vision Transformer Lidar Sensor Vehicular Network mmWave Signal Millimeter Wave Line of Sight ViT LoS V2X Blockage Prediction

March 7, 2024

ACC-ViT : Atrous Convolution's Comeback in Vision Transformers
Nabil Ibtehaz, Ning Yan, Masood Mortazavi, Daisuke Kihara
Vision Transformer Sparse Attention Vision Backbone ViT LoS V2X Atrous Convolution

February 28, 2024

STC-ViT: Spatio Temporal Continuous Vision Transformer for Weather Forecasting
Hira Saleem, Flora Salim, Cormac Purcell
Vision Transformer Numerical Weather Prediction Weather Forecasting Weather Prediction Model ViT LoS V2X Continuous Weather

November 16, 2023

DINO-VITS: Data-Efficient Zero-Shot TTS with Self-Supervised Speaker Verification Loss for Noise Robustness
Vikentii Pankov, Valeria Pronina, Alexander Kuzmin, Maksim Borisov, Nikita Usoltsev, Xingshan Zeng, Alexander Golubkov, Nikolai Ermolenko, Aleksandra Shirshova, Yulia Matveeva
Text to Speech Speech Encoder Self Supervised Speech Model Zero Shot Text to Speech Noise Robustness Self Supervised Speaker Verification ViT LoS V2X

October 27, 2022

ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks
Zohreh HajiAkhondi-Meybodi, Arash Mohammadi, Ming Hou, Jamshid Abouei, Konstantinos N. Plataniotis
Pre Trained ViT Popularity Prediction Edge Caching Cross Attention Fusion Parallel Transformer ViT LoS V2X ViT Architecture

September 19, 2022

ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection
Yunsheng Ma, Ziran Wang
Vision Transformer Emotion Recognition Driver Distraction Computer Vision Technology ViT LoS V2X Multi Task Vision Transformer

July 12, 2022

eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation
Lu Yu, Wei Xiang, Juan Fang, Yi-Ping Phoebe Chen, Lianhua Chi
Vision Transformer Transformer Model Weakly Supervised Semantic Segmentation Novel Vision Transformer ViT LoS V2X Interpretable Transformer

May 27, 2022

X-ViT: High Performance Linear Vision Transformer without Softmax
Jeonggeun Song, Heung-Chang Lee
Vision Transformer Self Attention Transformer Based Model Computer Vision Task Softmax Function Dense Prediction Task ViT LoS V2X

March 22, 2022

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers
Ryan Grainger, Thomas Paniagua, Xi Song, Naresh Cuntoor, Mun Wai Lee, Tianfu Wu
Vision Transformer Semantic Segmentation Patch Attention ViT LoS V2X

March 20, 2022

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
Runsheng Xu, Hao Xiang, Zhengzhong Tu, Xin Xia, Ming-Hsuan Yang, Jiaqi Ma
Vision Transformer Autonomous Vehicle Multi Agent Cooperative Perception V2X Communication ViT LoS V2X V2X Perception

ViT LoS V2X

Papers

L-VITeX: Light-weight Visual Intuition for Terrain Exploration

CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications

ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks

ACC-ViT : Atrous Convolution's Comeback in Vision Transformers

STC-ViT: Spatio Temporal Continuous Vision Transformer for Weather Forecasting

DINO-VITS: Data-Efficient Zero-Shot TTS with Self-Supervised Speaker Verification Loss for Noise Robustness

ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks

ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection

eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation

X-ViT: High Performance Linear Vision Transformer without Softmax

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer