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Iterative Alignment Network for Continuous Sign Language Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| alternate optimization of the feature extractor and sequence model over iterations (ablation) | Iterative Alignment Network · RWTH-PHOENIX-Weather-2014 test | WER: 57.90 -> 36.71 | Helped |
| add soft-DTW alignment and use both decoders (ablation) | Iterative Alignment Network · RWTH-PHOENIX-Weather-2014 | WER | Helped |
| use only the CTC decoder instead of both aligned decoders (ablation) | Iterative Alignment Network · RWTH-PHOENIX-Weather-2014 test | WER: 37.9 -> 36.7 | Hurt |
| use only the LSTM decoder instead of both aligned decoders (ablation) | Iterative Alignment Network · RWTH-PHOENIX-Weather-2014 test | WER: 46.1 -> 36.7 | Hurt |
| train the aligned network end-to-end rather than alternating optimization (ablation) | Iterative Alignment Network · RWTH-PHOENIX-Weather-2014 test | WER: 69.3 -> 36.7 | Hurt |
Closest papers by meaning
- Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization (CVPR 2017 · similarity 0.92)
- TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions (AAAI 2024 · similarity 0.88)
- KD-MSLRT: Lightweight Sign Language Recognition Model Based on Mediapipe and 3D to 1D Knowledge Distillation (AAAI 2025 · similarity 0.85)
- Cross-Sentence Gloss Consistency for Continuous Sign Language Recognition (AAAI 2024 · similarity 0.84)
- SignGraph: A Sign Sequence is Worth Graphs of Nodes (CVPR 2024 · similarity 0.84)
- Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment (AAAI 2024 · similarity 0.84)
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