Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition

In this paper, we propose a novel Transformer-based approach for continuous sign language recognition (CSLR) from videos, aiming to address the shortcomings of traditional Transformers in learning local semantic context of SL. Specifically, the proposed relies on two distinct components: (a) a window-based RNN module to capture local temporal context and (b) a Transformer encoder, enhanced with local modeling via Gaussian bias and relative position information, as well as with global structure modeling through multi-head attention. To further improve model performance, we design a multimodal framework that applies the proposed to both appearance and motion signing streams, aligning their posteriors through a guiding CTC technique. Further, we achieve visual feature and gloss sequence alignment by incorporating a knowledge distillation loss. Experimental evaluation on two popular German CSLR datasets, demonstrates the superiority of our model.