# Global-local Enhancement Network for NMFs-aware Sign Language Recognition

arXiv: https://arxiv.org/abs/2008.10428 · HTML: https://mlsanity.com/paper/2008.10428/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 30 of 38)

- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Natural Language-Assisted Sign Language Recognition](https://mlsanity.com/paper/2303.12080/) (2023)
- [BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization](https://mlsanity.com/paper/2302.05075/) (2023)
- [OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages](https://mlsanity.com/paper/2110.05877/) (2021)
- [Improving Gloss-free Sign Language Translation by Reducing Representation Density](https://mlsanity.com/paper/2405.14312/) (2024)
- [Scaling up Multimodal Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2408.08544/) (2024)
- [Automatic dense annotation of large-vocabulary sign language videos](https://mlsanity.com/paper/2208.02802/) (2022)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Towards Online Continuous Sign Language Recognition and Translation](https://mlsanity.com/paper/2401.05336/) (2024)
- [Sign Language Production with Latent Motion Transformer](https://mlsanity.com/paper/2312.12917/) (2023)
- [MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition](https://mlsanity.com/paper/2405.20666/) (2024)
- [Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition](https://mlsanity.com/paper/2110.12396/) (2021)
- [Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation](https://mlsanity.com/paper/2506.00129/) (2025)
- [PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution](https://mlsanity.com/paper/2107.12600/) (2021)
- [Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal](https://mlsanity.com/paper/2212.13023/) (2022)
- [StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition](https://mlsanity.com/paper/2212.12857/) (2022)
- [Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition](https://mlsanity.com/paper/2406.10501/) (2024)
- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)
- [SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval](https://mlsanity.com/paper/2407.16394/) (2024)
- [Multi-scale temporal network for continuous sign language recognition](https://mlsanity.com/paper/2204.03864/) (2022)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [Logos as a Well-Tempered Pre-train for Sign Language Recognition](https://mlsanity.com/paper/2505.10481/) (2025)
- [Dual-view Spatio-Temporal Feature Fusion with CNN-Transformer Hybrid Network for Chinese Isolated Sign Language Recognition](https://mlsanity.com/paper/2506.06966/) (2025)
- [M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production](https://mlsanity.com/paper/2603.23617/) (2026)
- [Cross-Modal Consistency Learning for Sign Language Recognition](https://mlsanity.com/paper/2503.12485/) (2025)
- [SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform](https://mlsanity.com/paper/2206.12420/) (2022)

## Abstract (arXiv)

Sign language recognition (SLR) is a challenging problem, involving complex manual features, i.e., hand gestures, and fine-grained non-manual features (NMFs), i.e., facial expression, mouth shapes, etc. Although manual features are dominant, non-manual features also play an important role in the expression of a sign word. Specifically, many sign words convey different meanings due to non-manual features, even though they share the same hand gestures. This ambiguity introduces great challenges in the recognition of sign words. To tackle the above issue, we propose a simple yet effective architecture called Global-local Enhancement Network (GLE-Net), including two mutually promoted streams towards different crucial aspects of SLR. Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. Moreover, due to the lack of datasets explicitly focusing on this kind of features, we introduce the first non-manual-features-aware isolated Chinese sign language dataset~(NMFs-CSL) with a total vocabulary size of 1,067 sign words in daily life. Extensive experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of our method.