Papers › Sign Language Recognition via Deformable 3D Convolutions and Modulated Graph...
Sign Language Recognition via Deformable 3D Convolutions and Modulated Graph Convolutional Networks
Katerina Papadimitriou, Gerasimos Potamianos
Automatic sign language recognition (SLR) remains challenging, especially when employing RGB video alone (i.e., with no depth or special glove-based input) and under a signer-independent (SI) framework, due to inter-personal signing variation. In this paper, we address SI isolated SLR from RGB video, proposing an innovative deep-learning framework that leverages multi-modal appearanceand skeleton-based information. Specifically, we propose three components for the first time in SLR: (i) a modified version of the ResNet2+1D network to capture signing appearance information, where spatial and temporal convolutions are substituted by their deformable counterparts, accomplishing both prevalent spatial modeling potential and motion-aware modeling adaptability; (ii) a novel spatio-temporal graph convolutional network (ST-GCN) that integrates a GCN variant, involving weight and affinity modulation for modeling diverse correlations between different body joints beyond the physical human skeleton structure, followed by a self-attention layer and a temporal convolution; and (iii) the “PIXIE” 3D human pose and shape regressor to generate 3D joint-rotation parameterization used for ST-GCN graph construction. Both appearance- and skeleton-based streams are ensembled in the proposed system and evaluated on two datasets of isolated signs, one in Turkish and one in Greek. Our system outperforms the state-of-the-art on the second set, yielding 53% relative error rate reduction (2.45% absolute), while it performs on par with the best reported system on the first.
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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 | AUTSL | 3D-DCNN + ST-MGCN | Rank-1 Recognition Rate | 0.9842 | #3 of 9 | Archive leaderboard | report |
| Sign Language Recognition | GSL | 3D-DCNN + ST-MGCN | Rank-1 Recognition Rate | 0.9785 | #1 of 1 | 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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