FEA-SLT: A Gloss-Free End-to-End Framework for Facial-Expression-Aware Sign Language Translation

arXiv:2601.03549 · cs.CV, cs.CL · Submitted 2026-01-07 · Read on arXiv

cs.CV, cs.CL

Submitted: 2026-01-07

Updated: 2026-08-30

Code: https://github.com/TuGuobin/FEA-SLT

Terminology

• Sign Language Translation — 29× in this paper
• Ablation — 21× in this paper · explained in 1 episode
• Alignment — 13× in this paper · explained in 1 episode
• Ablation study — 12× in this paper · explained in 1 episode
• Feature Extraction — 10× in this paper · explained in 2 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Adaptation — 7× in this paper · explained in 1 episode
• Facial-Expression Feature — 7× in this paper
• Facial-Expression-Aware Fusion — 7× in this paper
• Embeddings — 6× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Manual Signals — 4× in this paper
• Redundancy — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• International Conference on Learning Representations — 3× in this paper
• Modality Gap — 3× in this paper · explained in 4 episodes
• Non-Manual Signals — 3× in this paper
• Grounding — 2× in this paper · explained in 3 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• International Conference on Machine Learning — 2× in this paper
• Multimodal Alignment — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment Pre-training — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Facial Expression Recognition — 1× in this paper
• Facial-Expression-Aware Modulation — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Temporal Convolutional Network — 1× in this paper
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• EmoSign: A Multimodal Dataset for Understanding Emotions in American Sign Language
• Perspectives on Capturing Emotional Expressiveness in Sign Language
• Scaling Instruction-Finetuned Language Models
• Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation
• GPT-4 Technical Report
• When Do We Not Need Larger Vision Models?
• All You Need In Sign Language Production

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