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Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space
Ilias Papastratis, Kosmas Dimitropoulos, DIMITRIOS KONSTANTINIDIS, Petros Daras
Continuous Sign Language Recognition (CSLR) refers to the challenging problem of recognizing sign language glosses and their temporal boundaries from weakly annotated video sequences. Previous methods focus mostly on visual feature extraction neglecting text information and failing to effectively model the intra-gloss dependencies. In this work, a cross-modal learning approach that leverages text information to improve vision-based CSLR is proposed. To this end, two powerful encoding networks are initially used to produce video and text embeddings prior to their mapping and alignment into a joint latent representation. The purpose of the proposed cross-modal alignment is the modelling of intra-gloss dependencies and the creation of more descriptive video-based latent representations for CSLR. The proposed method is trained jointly with video and text latent representations. Finally, the aligned video latent representations are classified using a jointly trained decoder. Extensive experiments on three well-known sign language recognition datasets and comparison with state-of-the-art approaches demonstrate the great potential of the proposed approach.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sign Language Recognition | RWTH-PHOENIX-Weather 2014 | CrossModal | Word Error Rate (WER) | 24.0 | #17 of 22 | Archive leaderboard | report |
| Sign Language Recognition | RWTH-PHOENIX-Weather 2014 T | CrossModal | Word Error Rate (WER) | 24.3 | #14 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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