The end-to-end attention-estimation pipeline meets the one-second prediction stride on an NVIDIA A100 GPU, with warm latency of 50.65 ms per window and 1.02 ms for the temporal model¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? On an NVIDIA A100-SXM4-40GB GPU, the warm end-to-end pipeline averaged 50.65 ms per prediction window (19.7 predictions per second), and the temporal fairness model (402,531 trainable parameters) required 1.02 ms on average for prediction. → Christoforos Fragkiadakis 2026

Evidence¶

Christoforos Fragkiadakis 2026¶

Christoforos Fragkiadakis, Seyed Sahand Mohammadi Ziabari, and Ali Mohammed Mansoor Alsahag. (2026). Fairness-Aware Multimodal Transformer Modeling for Real-Time Student Attention Estimation. https://arxiv.org/abs/2609.02232 doi:10.48550/arxiv.2609.02232

q2 · i? · design · r2

Deployment feasibility benchmark on an unseen subject recording; warm latency included preprocessing, image–sensor alignment, CLIP ViT-L/14 feature extraction, window construction, and prediction, with cold-start initialization excluded.

"the warm end-to-end pipeline achieved a mean latency of 50.65 ms per prediction window at a one-second stride on an NVIDIA A100-SXM4-40GB GPU, corresponding to a throughput of 19.7 predictions per second."

Discussion¶

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