Hands-On: Segmenting Individual Signs from Continuous Sequences
This addresses sign language translation and data annotation, but appears incremental as it builds on existing transformer and BIO tagging methods.
The paper tackles continuous sign language segmentation by proposing a transformer-based architecture with BIO tagging, achieving state-of-the-art results on the DGS Corpus and surpassing benchmarks on BSLCorpus.
This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and data annotation. We propose a transformer-based architecture that models the temporal dynamics of signing and frames segmentation as a sequence labeling problem using the Begin-In-Out (BIO) tagging scheme. Our method leverages the HaMeR hand features, and is complemented with 3D Angles. Extensive experiments show that our model achieves state-of-the-art results on the DGS Corpus, while our features surpass prior benchmarks on BSLCorpus.