SF-Net: Structured Feature Network for Continuous Sign Language Recognition

Abstract

Continuous sign language recognition (SLR) aims to translate a signing sequence into a sentence. It is very challenging as sign language is rich in vocabulary, while many among them contain similar gestures and motions. Moreover, it is weakly supervised as the alignment of signing glosses is not available. In this paper, we propose Structured Feature Network (SF-Net) to address these challenges by effectively learn multiple levels of semantic information in the data. The proposed SF-Net extracts features in a structured manner and gradually encodes information at the frame level, the gloss level and the sentence level into the feature representation. The proposed SF-Net can be trained end-to-end without the help of other models or pre-training. We tested the proposed SF-Net on two large scale public SLR datasets collected from different continuous SLR scenarios. Results show that the proposed SF-Net clearly outperforms previous sequence level supervision based methods in terms of both accuracy and adaptability.

arXiv comment12 pages, 8 figures

References in corpus (2)

• Sequence to Sequence Learning with Neural Networks
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Cited by in corpus (6)

• A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition
• Quantitative Survey of the State of the Art in Sign Language Recognition
• PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution
• ASL Recognition with Metric-Learning based Lightweight Network
• Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition
• Master Thesis: Neural Sign Language Translation by Learning Tokenization