Toward Generic Cross-Modal Transmission Strategy
Bibliographic record
Abstract
Multi-modal services, integrating various modalities such as audio, visual, and haptic, have emerged as leading multimedia applications in the 5G era and beyond. To fulfill the demands for low latency, high reliability, and large capacity, cross-modal transmission schemes have been proposed. Typically, these schemes emphasize on either audio-visual or haptic modality, and prioritize flawless transmission of one modality to assist the other modality streaming. However, these prerequisite and assumption do not hold for generic multi-modal services and communication environments, where determining the priority of modality and guaranteeing flawless transmission becomes challenging. To address this fundamental problem, in this paper, we introduce a strategy toward generic cross-modal transmission, enabling visual and haptic modalities to assist each other as needed. The strategy includes a visual-haptic mutual stream delivery mechanism at the sender and a visual-haptic mutual signal reconstruction approach at the receiver. The former aims to eliminate redundancy in visual and haptic streams through mutual assistance, while the latter adaptively handles impaired, missing, or delayed visual or haptic signals by leveraging modality-aware knowledge transfer and semantic-aware signal generation techniques. The proposed strategy demonstrates excellent performance through experiments conducted on a standard multi-modal dataset and a practical visual-haptic communication platform.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".