Papers › Dense Temporal Convolution Network for Sign Language Translation
Dense Temporal Convolution Network for Sign Language Translation
Dan Guo; Shuo Wang; Qi Tian;Meng Wang
The sign language translation (SLT) which aims at translating a sign language video into natural language is weakly supervised given that there is no exact mapping relationship between visual actions and textual words in a sentence label. To align the sign language actions and translate them into the respective words automatically, this paper proposes a dense temporal convolution network, termed \emph{DenseTCN} which captures the actions in hierarchical views. Within this network, a temporal convolution (TC) is designed to learn the short-term correlation among adjacent features and further extended to a dense hierarchical structure. In the kᵗʰ TC layer, we integrate the outputs of all preceding layers together: (1) The TC in a deeper layer essentially has larger receptive fields, which captures long-term temporal context by the hierarchical content transition. (2) The integration addresses the SLT problem by different views, including embedded short-term and extended long-term sequential learning. Finally, we adopt the CTC loss and a fusion strategy to learn the feature-wise classification and generate the translated sentence. The experimental results on two popular sign language benchmarks, \emph{i.e.} PHOENIX and USTC-ConSents, demonstrate the effectiveness of our proposed method in terms of various measurements.
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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 | DTN | Word Error Rate (WER) | 36.5 | #20 of 22 | 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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