Related Experiment Video
Updated: Feb 8, 2026

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A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
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Foundation Model Empowered Real-Time Video Conference With Semantic Communications
Summary
This study introduces a novel framework for real-time video conferences using foundation models. It significantly enhances interactivity and achieves high compression ratios with excellent semantic fidelity in video transmission.
Area of Science:
- Computer Vision for Communications
- Multimedia Services
- Artificial Intelligence Foundation Models
Background:
- Real-time video conferences generate substantial traffic due to proliferating interactive multimedia services.
- Existing Computer Vision (CV) techniques lack integration for diverse interactive video tasks like recognition and segmentation.
- The rise of foundation models presents opportunities for task-oriented semantic communications.
Purpose of the Study:
- To propose a novel framework, Real-Time Video Conference with Foundation Model (RTVCFM), for enhanced interactivity in multimedia services.
- To address the limitations of current CV methods in handling complex interactive video demands.
- To leverage foundation models for efficient and high-fidelity video transmission.
Main Methods:
- At the transmitter: Causal understanding and spatiotemporal decoupling using Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA), and Segment Anything Model 2 (SAM2) for semantic segmentation.
- During transmission: Two-stage semantic transmission optimization driven by Channel State Information (CSI) for low bit rate and high semantic fidelity.
- At the receiver: Multidimensional fusion using Diffusion Model for Foreground Background Fusion (DMFBF) for video stream reconstruction.
Main Results:
- Achieved a compression ratio as high as 95.6%.
- Guaranteed high semantic similarity with 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM).
- Demonstrated that reconstructed videos are highly similar to original videos.
Conclusions:
- RTVCFM effectively satisfies the interactivity requirements of real-time multimedia services.
- The proposed framework enables significant video compression while preserving high semantic fidelity.
- This approach showcases the potential of foundation models in advancing computer vision for communications.
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