SemConf: A System for Multiparty Semantic Video Conferencing
Bibliographic record
Abstract
Multi-party real-time video conferencing has become an indispensable service in industrial production and daily life. However, the current dynamic and limited network resources can no longer meet the growing service demands of users, resulting lagging and low visual quality. The emerging semantic transmission, together with the network-wide redundant computation capacity, provides new opportunities towards a new paradigm of semantic video conferencing. The key challenge of such fusion lies in the interplay of traditional streaming adaptation and the new semantic processing, calling for a holistic mechanism to optimize the service provision with compatibility and efficiency. In this paper, we for the first time address this challenge, and propose SemConf, a novel framework that integrate the semantic transmission into the video conferencing towards optimal user QoE. Our extensive evaluations, against state-of-the-art baselines, reveal that SemConf achieves a substantial improvement in QoE, with up to 33.6% enhancement in bandwidth-constrained environment. Overall, this work highlights the critical role of the coordination algorithm in balancing computational load and network throughput, showcasing SemConf as a transformative approach in the realm of semantic video conferencing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".