arXiv Computer Vision

Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks

The paper introduces KDG‑SemNOMA, a framework for 6G robotic vehicle networks that combines knowledge distillation and generative models to improve semantic communication over uplink non‑orthogonal multiple access (NOMA). It employs a ConvNeXt‑based deep joint source‑channel coding architecture with an enhanced attention feature module for dynamic channel adaptation, and uses an orthogonal teacher model to guide a NOMA student model via two‑stage knowledge distillation. A channel‑conditional GAN further refines the reconstructed images, yielding higher pixel‑level accuracy and perceptual fidelity on the FFHQ‑256 dataset compared to state‑of‑the‑art methods.

arXiv Computer Vision

Aug 31

GAN-Based Semantic Communication for Image Transmission in IoV

The paper introduces a GAN‑based semantic communication framework for image transmission in the Internet of Vehicles, aiming to overcome bandwidth and channel limitations. At the transmitter, a pyramid attention network extracts semantic label maps and a priority mechanism assigns weights to categories based on driving safety, guiding bit allocation and loss design. The receiver reconstructs images using a coarse‑to‑fine multi‑resolution generator, multi‑scale discriminator, temporal consistency, spatial pyramid pooling, and class‑aware convolutions, achieving high‑fidelity results with combined adversarial, feature‑matching, and perceptual losses. Experiments on Cityscapes demonstrate superior semantic segmentation accuracy and image quality compared to existing methods, with stable performance under AWGN and Rayleigh channels.

By Ruixing Ren, Shan Chen, Junhui Zhao, Xiaoke Sun

diffusion computer-vision safety

Hugging Face Trending Papers

Aug 11

Grid-Preserving Knowledge Distillation: Transferring Convolutional Inductive Bias to Vision Transformers under Data Scarcity

Vision Transformers underperform convolutional networks when training data is scarce, and distilling convolutional inductive biases from a CNN teacher is an effective remedy that leaves the deployed model unchanged. General-purpose feature distillation, however, transfers little in this setting.

llms efficiency benchmarks safety

arXiv Computer Vision

Sep 17

KDTwin: Task-Aware Knowledge Distillation for Lightweight Multi-Task Driving Scene Segmentation

arXiv:2609.18955v1 Announce Type: new Abstract: Efficient perception models are essential for real-time autonomous driving, where accuracy and computational cost must be carefully balanced. However,...

By Huy Che, Minh-Khoi Do, Dinh-Duy Phan, Duc-Khai Lam

llms agents computer-vision robotics efficiency

arXiv Machine Learning

Jul 16

A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles

arXiv:2607. 13494v1 Announce Type: new Abstract: The development of smart transportation systems and the introduction of 6G wireless communication technologies have significantly changed vehicle network topologies.

By S. M. Abtahiul Alam, Niloy Das, Apurba Adhikary, Yu Qiao, Zhu Han, Choong Seon Hong

agents robotics safety

arXiv Computer Vision

Sep 22

Towards robust multimodal 3D object detection via visual foundation models

arXiv:2609.23541v1 Announce Type: new Abstract: Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and...

By Ziying Song, Lin Liu, Hongyu Pan, Shaoqing Xu, Lei Yang, Mingzhe Guo, Caiyan Jia

agents computer-vision robotics multimodal benchmarks

arXiv Machine Learning

Aug 11

Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices

arXiv:2602. 22794v2 Announce Type: replace Abstract: Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions.

By Soroosh Miri, Sepehr Abolhasani, Shahrokh Farahmand, S. Mohammad Razavizadeh, Jiguang He

arXiv Computer Vision

Sep 1

Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation

arXiv:2409.11018v3 Announce Type: replace Abstract: The LiDAR 3D object detector that balances accuracy and speed is crucial for achieving real-time perception in autonomous driving. However, many ex...

By Rui Yu, Runkai Zhao, Jiagen Li, Qingsong Zhao, HuaiCheng Yan, Meng Wang

llms agents computer-vision robotics efficiency safety

arXiv AI

Jul 7

BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations

arXiv:2603. 06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are essential for handling complex decision-making and long-tail scenarios.

By Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen, Sihao Ding

llms agents computer-vision robotics safety

arXiv Computer Vision

Sep 22

LiAuto-MindViT: A Hybrid Vision Backbone with Adaptive Bidirectional Mamba

arXiv:2609.24337v1 Announce Type: new Abstract: While Mamba-based models have shown strong potential for long sequence modeling, adapting them to vision is challenging due to the requirement of local...

By Lifu Mu, Shuai Chen, Wen Zheng, Haoyi Sun, Xueyang Fu, Pengfei Yu, Ning Mao, Tao Wei, Zhou Pan

llms computer-vision benchmarks safety

arXiv Machine Learning

Jun 30

Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding

arXiv:2604. 07753v2 Announce Type: replace-cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts.

By Xiangyue Liu, Zijian Zhang, Miles Yang, Zhao Zhong, Liefeng Bo, Ping Tan

llms diffusion multimodal benchmarks

arXiv AI

Jun 6

Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving

arXiv:2601. 21288v2 Announce Type: replace Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain.

By Weitong Lian, Zecong Tang, Haoran Li, Tianjian Gao, Yifei Wang, Zixu Wang, Lingyi Meng, Tengju Ru, Zhejun Cui, Yichen Zhu, Hangshuo Cao, Qi Kang, Tianxing Chen, Kaixuan Wang, Yu Zhang

llms agents robotics fine-tuning efficiency safety

arXiv AI

Jun 6

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.

By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang

agents diffusion robotics fine-tuning multimodal benchmarks safety

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