Boundary-Aware Sentence-Gloss Alignment with Semantic Similarity Measurement for Continuous Sign Language Recognition

Continuous sign language recognition (CSLR) plays a crucial role in facilitating communication between deaf and hearing individuals. A key aspect of achieving precise CSLR is the alignment of the video segment of each sign with its gloss, namely its corresponding text representation in natural language. However, the coarticulation phenomenon, where contextual dependencies between adjacent signs blur the boundaries of individual signs, poses a significant challenge to this task. In this paper, we propose a novel boundary-aware sentence-gloss alignment network for CSLR to address this challenge. Our network first designs a task-relevant boundary-aware similarity measurement, evaluating sign frames by both appearance and their recognition contribution, mitigating coarticulation-induced transition noise to restore precise boundaries. For enhanced alignment, we propose a hierarchical sentence-gloss alignment: coarse sentence-level alignment reduces cross-modal disparity, while fine-grained gloss-level alignment refines video-to-token mapping. Finally, an adaptive class-divergence loss sharpens gloss decoding by maximizing inter-class discrimination. Our proposed framework provides a simple and effective solution to mitigate the boundary ambiguity caused by coarticulation, optimizing continuous sign language recognition algorithms from a new perspective. Extensive experiments conducted on four public sign language recognition (SLR) datasets demonstrate that our proposed boundary-aware sentence-gloss alignment network learns precise alignments and achieves state-of-the-art performance.