Towards Better Transition Modeling in Recurrent Neural Networks: the Case of Sign Language Tokenization
Pierre Poitier, Jérôme Fink, Benoît Frénay · 2022
Recurrent neural networks can be used to segment sequences such as videos, where transitions can be challenging to detect.This paper benchmarks strategies to better model the transition between states.The specific task of SL video tokenization is chosen for the evaluation, as it remains challenging.Tokenizers are the cornerstone of natural language processing pipelines.There exist powerful tokenizers for text data, but sign language (SL) video tokenizers are still under development.Benchmarked strategies prove to be useful to improve SL videos tokenization, but there is still room for improvement to better model state transitions.