AI & ML
Token Importance-Aware Semantic Communication Enabled Robust Video Transmission
arXiv eess.IV0 views
arXiv:2611.12928v1 Announce Type: new Abstract: Video applications are increasingly prevalent, yet their high data volume and temporal dependency make robust wireless transmission challenging under limited communication resources. To address this issue, we propose a token importance-aware semantic communication (Ti-SemCom) framework for bandwidth-efficient video transmission. Specifically, object and motion information is jointly extracted to identify communication-relevant regions, which are hierarchically associated with semantic tokens according to their importance. The transmitted tokens are then dynamically selected based on both token importance and the available bandwidth, prioritizing more informative video content under resource constraints. At the receiver, untransmitted regions reuse recently reconstructed references to further reduce redundant transmission. Experiments on the CAVIAR dataset show that Ti-SemCom can achieve an 88.3\% reduction in communication cost while maintaining competitive reconstruction quality.
Source:https://arxiv.org/abs/2611.12928PreviousPygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry ReconstructionNextIs a CNN-Transformer Ensemble Worth It When a Chest X-Ray Foundation Model Is Available?
Related posts
HPE7-A11 Exam: A Design-Artifact Guide to Campus Access Architecture
0dev.to

Railway's Breach Cost $740,000 in Fraudulent AI Charges — and Up to 100,000 Workspaces May Have Leaked Secrets
0dev.to
Nod Reports 98% Agent Task Completion and 93.2% Long-Term Memory Recall in Tests
0dev.to
How to get business location data for a whole US state in bulk
0dev.to
OpenAI Cost Tracking Middleware: Build a Logging Wrapper That Shows Every Call's Price
0dev.to

3,500 laser-armed AI submarine drones patrol Norwegian salmon farms, killing sea lice and protecting 112 million fish
0Tom's Hardware