In this work, the authors propose a method called BEST (BERT Pre-Training for Sign Language Recognition with Coupling Tokenization) to improve sign language recognition (SLR) using BERT pre-training. They leverage the success of BERT pre-training and model domain-specific statistics to enhance the SLR model. Since hand and body movements are dominant in sign language expression, they organize them as pose triplet units and feed them into the Transformer backbone in a frame-wise manner. To perform pre-training, the authors reconstruct the masked triplet unit from the corrupted input sequence. This helps learn hierarchical correlation context cues among internal and external triplet units. However, unlike BERT's highly semantic word tokens, pose units are low-level signals originally located in continuous space. This prevents the direct adoption of BERT's cross-entropy objective. To bridge this semantic gap, they introduce coupling tokenization of the triplet unit. This adaptively extracts discrete pseudo labels from the pose triplet unit, representing semantic gesture/body states. After pre-training, the authors fine-tune the pre-trained encoder on downstream SLR tasks along with a newly added task-specific layer. Extensive experiments are conducted to validate their proposed method, achieving new state-of-the-art performance on four benchmarks with a notable gain. In related work, sign language recognition has been studied extensively in recent years using RGB-based methods and pose-based methods. RGB based methods focus on visual information captured by cameras while pose based methods analyze body joint positions obtained from depth sensors or 2D/3D poses estimated from RGB images. The proposed BEST method addresses some limitations of existing approaches by leveraging BERT pre training and incorporating domain specific statistics through pose triplet units. By bridging the semantic gap between low level pose units and high level semantics required for SLR tasks their method achieves improved performance compared to previous state of the art methods. Overall this work contributes to advancing sign language recognition by effectively utilizing pre training techniques and modeling domain specific statistics leading to more accurate and robust SLR models.
- Authors propose a method called BEST to improve sign language recognition using BERT pre-training
- BEST leverages the success of BERT pre-training and model domain-specific statistics
- Hand and body movements are organized as pose triplet units and fed into the Transformer backbone in a frame-wise manner
- Pre-training involves reconstructing masked triplet units from corrupted input sequences to learn hierarchical correlation context cues
- Coupling tokenization is introduced to bridge the semantic gap between low-level pose units and high-level semantics required for SLR tasks
- After pre-training, fine-tuning is performed on downstream SLR tasks with a newly added task-specific layer
- Extensive experiments validate the proposed method, achieving new state-of-the-art performance on four benchmarks
- RGB-based methods and pose-based methods have been studied extensively in sign language recognition
- BEST addresses limitations of existing approaches by leveraging BERT pre-training and incorporating domain-specific statistics through pose triplet units
- The method achieves improved performance compared to previous state-of-the-art methods
Authors propose a new method called BEST to help computers understand sign language better. They use a special technique called BERT pre-training to make the computer smarter. The computer learns how people move their hands and bodies in sign language, and uses this information to recognize signs. The computer also learns how different movements are connected and can understand the meaning behind them. This method is better than other methods because it combines different techniques and achieves better results."
Definitions:
- Authors: People who write books or articles.
- Method: A way of doing something.
- Sign language: A way of communicating using hand and body movements instead of words.
- BERT pre-training: A special technique that helps computers learn more about language.
- Pose triplet units: Different movements made by the body when using sign language.
- Transformer backbone: The main part of the computer program that processes information.
- Pre-training: Teaching the computer basic knowledge before it learns more specific things.
- Semantic gap: The difference between low-level movements and high-level meanings in sign language.
- Fine-tuning: Making small adjustments to improve the performance of a computer program.
- State-of-the-art methods: The most advanced ways of doing something at the moment.
Improving Sign Language Recognition with BERT Pre-Training
Sign language recognition (SLR) is an important area of research that has been studied extensively in recent years. SLR models are used to recognize and interpret sign language gestures from cameras or depth sensors, enabling better communication between deaf people and the hearing world. In this work, the authors propose a method called BEST (BERT Pre-Training for Sign Language Recognition with Coupling Tokenization) to improve SLR using BERT pre-training.
Background
In related work, sign language recognition has been studied extensively using RGB-based methods and pose-based methods. RGB based methods focus on visual information captured by cameras while pose based methods analyze body joint positions obtained from depth sensors or 2D/3D poses estimated from RGB images. However, both approaches have their limitations when it comes to recognizing complex hand and body movements which are dominant in sign language expression.
The Proposed Method: BEST
To address these limitations, the authors propose a new method called BEST which leverages the success of BERT pre-training and model domain specific statistics to enhance the SLR model. The proposed method organizes hand and body movements as pose triplet units which are then fed into a Transformer backbone in a frame wise manner. To perform pre training, they reconstruct masked triplet units from corrupted input sequences so as to learn hierarchical correlation context cues among internal and external triplet units.
However, unlike BERT's highly semantic word tokens, pose units are low level signals originally located in continuous space which prevents direct adoption of BERT's cross entropy objective for pre training purposes. To bridge this semantic gap they introduce coupling tokenization of the triplet unit which adaptively extracts discrete pseudo labels representing semantic gesture/body states from each pose triplet unit . After pre training is complete they fine tune the encoder on downstream SLR tasks along with a newly added task specific layer .
Experimental Results
Extensive experiments were conducted to validate their proposed method achieving new state of the art performance on four benchmarks with notable gain compared to previous methods .
Conclusion
Overall this work contributes to advancing sign language recognition by effectively utilizing pre training techniques and modeling domain specific statistics leading to more accurate and robust SLR models .
