Weighted Multi-modal Sign Language Recognition

Multiple modalities can boost accuracy in the difficult task of Sign Language Recognition (SLR), however, each modality does not necessarily contribute the same quality of information. Current multi-modal approaches assign the same importance weightings to each modality, or set weightings based on unproven heuristics. This paper takes a systematic approach to find the optimal weights by performing grid search. Firstly, we create a multi-modal version of the RGB only WLASL100 data with additional hand crop and skeletal pose modalities. Secondly, we create a 3D CNN based weighted multi-modal sign language network (WMSLRnet). Finally, we run various grid searches to find the optimal weightings for each modality. We show that very minor adjustments in the weightings can have major effects on the final SLR accuracy. On WLASL100, we significantly outperform previous networks of similar design, and achieve high accuracy in SLR without highly complex pre-training schemes or extra data.