Hands-On: Segmenting Individual Signs from Continuous Sequences 事件

Hands-On: Segmenting Individual Signs from Continuous Sequences arXiv:2504.08593v5 Announce Type: replace Abstract: 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