Practical sign language translation requires co-designing the ML model with the deployment infrastructure—optimizing latency through system-level techniques (streaming, parallelization, state machines) matters as much as model accuracy for real-time applications.
This paper tackles real-time sign language translation at the sentence level by fine-tuning a SHuBERT-ByT5 model on a subset of How2Sign data using QLoRA.