# SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding

arXiv: https://arxiv.org/abs/2305.04868 · HTML: https://mlsanity.com/paper/2305.04868/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 103 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2008.09309/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning](https://mlsanity.com/paper/2104.14558/) (2021)
- [3D Hand Shape and Pose from Images in the Wild](https://mlsanity.com/paper/1902.03451/) (2019)

## Built on by (most cited first, 30 of 59)

- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark](https://mlsanity.com/paper/2310.20436/) (2023)
- [SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction](https://mlsanity.com/paper/2411.16765/) (2024)
- [Scaling up Multimodal Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2408.08544/) (2024)
- [Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator](https://mlsanity.com/paper/2411.17799/) (2024)
- [Sign Language Translation with Iterative Prototype](https://mlsanity.com/paper/2308.12191/) (2023)
- [A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars](https://mlsanity.com/paper/2401.04730/) (2024)
- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)
- [Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation](https://mlsanity.com/paper/2411.16810/) (2024)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [Towards Online Continuous Sign Language Recognition and Translation](https://mlsanity.com/paper/2401.05336/) (2024)
- [MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition](https://mlsanity.com/paper/2405.20666/) (2024)
- [Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation](https://mlsanity.com/paper/2505.15438/) (2025)
- [Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation](https://mlsanity.com/paper/2506.00129/) (2025)
- [Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2405.05672/) (2024)
- [Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition](https://mlsanity.com/paper/2406.10501/) (2024)
- [Deep Understanding of Sign Language for Sign to Subtitle Alignment](https://mlsanity.com/paper/2503.03287/) (2025)
- [Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition](https://mlsanity.com/paper/2503.20436/) (2025)
- [CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition](https://mlsanity.com/paper/2504.01666/) (2025)
- [SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Space](https://mlsanity.com/paper/2504.16315/) (2025)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation](https://mlsanity.com/paper/2402.19118/) (2024)
- [A Chinese Continuous Sign Language Dataset Based on Complex Environments](https://mlsanity.com/paper/2409.11960/) (2024)
- [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)
- [Exploring Pose-based Sign Language Translation: Ablation Studies and Attention Insights](https://mlsanity.com/paper/2507.01532/) (2025)
- [Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video](https://mlsanity.com/paper/2308.04074/) (2023)
- [Continuous Sign Language Recognition with Adapted Conformer via Unsupervised Pretraining](https://mlsanity.com/paper/2405.12018/) (2024)
- [A Comparative Study of Continuous Sign Language Recognition Techniques](https://mlsanity.com/paper/2406.12369/) (2024)
- [Cross-Modal Consistency Learning for Sign Language Recognition](https://mlsanity.com/paper/2503.12485/) (2025)

## Abstract (arXiv)

Hand gesture serves as a crucial role during the expression of sign language. Current deep learning based methods for sign language understanding (SLU) are prone to over-fitting due to insufficient sign data resource and suffer limited interpretability. In this paper, we propose the first self-supervised pre-trainable SignBERT+ framework with model-aware hand prior incorporated. In our framework, the hand pose is regarded as a visual token, which is derived from an off-the-shelf detector. Each visual token is embedded with gesture state and spatial-temporal position encoding. To take full advantage of current sign data resource, we first perform self-supervised learning to model its statistics. To this end, we design multi-level masked modeling strategies (joint, frame and clip) to mimic common failure detection cases. Jointly with these masked modeling strategies, we incorporate model-aware hand prior to better capture hierarchical context over the sequence. After the pre-training, we carefully design simple yet effective prediction heads for downstream tasks. To validate the effectiveness of our framework, we perform extensive experiments on three main SLU tasks, involving isolated and continuous sign language recognition (SLR), and sign language translation (SLT). Experimental results demonstrate the effectiveness of our method, achieving new state-of-the-art performance with a notable gain.