Papers › SlowFast Network for Continuous Sign Language Recognition
SlowFast Network for Continuous Sign Language Recognition
Junseok Ahn, Youngjoon Jang, Joon Son Chung
The objective of this work is the effective extraction of spatial and dynamic features for Continuous Sign Language Recognition (CSLR). To accomplish this, we utilise a two-pathway SlowFast network, where each pathway operates at distinct temporal resolutions to separately capture spatial (hand shapes, facial expressions) and dynamic (movements) information. In addition, we introduce two distinct feature fusion methods, carefully designed for the characteristics of CSLR: (1) Bi-directional Feature Fusion (BFF), which facilitates the transfer of dynamic semantics into spatial semantics and vice versa; and (2) Pathway Feature Enhancement (PFE), which enriches dynamic and spatial representations through auxiliary subnetworks, while avoiding the need for extra inference time. As a result, our model further strengthens spatial and dynamic representations in parallel. We demonstrate that the proposed framework outperforms the current state-of-the-art performance on popular CSLR datasets, including PHOENIX14, PHOENIX14-T, and CSL-Daily.
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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 | CSL-Daily | SlowFastSign | Word Error Rate (WER) | 24.9 | #1 of 14 | Archive leaderboard | report |
| Sign Language Recognition | RWTH-PHOENIX-Weather 2014 | SlowFastSign | Word Error Rate (WER) | 18.3 | #1 of 22 | Archive leaderboard | report |
| Sign Language Recognition | RWTH-PHOENIX-Weather 2014 T | SlowFastSign | Word Error Rate (WER) | 18.7 | #1 of 15 | Archive leaderboard | report |
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