Continuous Sign Language Recognition with Motor Attention Mechanism and Self-Distillation

Concetti Chiave

The author proposes a novel motor attention mechanism to capture dynamic changes in sign language expressions, enhancing recognition accuracy. Additionally, the self-distillation method is applied to improve feature expression without increasing computational resources.

Sintesi

The content discusses a novel approach to Continuous Sign Language Recognition (CSLR) using a motor attention mechanism and self-distillation. The proposed model, MAM-FSD, combines these techniques to enhance inference ability and robustness. Experimental results show state-of-the-art accuracy on three datasets.
The paper highlights the importance of capturing dynamic changes between frames in sign language recognition. It introduces a motor attention mechanism that focuses on motion regions for improved accuracy. The self-distillation method enhances feature expression without additional computational resources.
Key points include the introduction of the MAM-FSD model combining motor attention and self-distillation methods for CSLR. Experiments demonstrate improved accuracy on publicly available datasets. Visualization experiments validate the effectiveness of the motor attention mechanism.

Statistiche

"Our proposed method can effectively extract the sign language motion information in videos."
"The experimental results show that our proposed method can effectively extract the sign language motion information in videos."
"The WER values reaches 19.2% and 18.8% on the validation and test sets respectively."

Citazioni

"The core purpose of this paper is to study the dynamic changes between frames, obtain a dynamic expression of image changes, capture distorted changes in local motion regions when generating sign language expressions, and improve the accuracy of sign language recognition."
"Our proposed method can effectively extract the sign language motion information in videos."

Approfondimenti chiave tratti da

Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation

by Qidan Zhu,Ji... alle arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19118.pdf

Domande più approfondite

How can motor attention mechanisms be applied to other areas beyond sign language recognition

Motor attention mechanisms can be applied to various areas beyond sign language recognition, such as action recognition in videos, gesture control interfaces, and human-computer interaction systems. In action recognition, motor attention mechanisms can help focus on key movement patterns for accurate classification. For gesture control interfaces, these mechanisms can enhance the understanding of hand gestures and improve interaction accuracy. In human-computer interaction systems, motor attention can aid in recognizing subtle movements or expressions for more intuitive user experiences.

What potential challenges or limitations might arise from relying heavily on dynamic features for recognition

Relying heavily on dynamic features for recognition may pose some challenges or limitations. One potential challenge is the increased computational complexity associated with processing dynamic information compared to static data. Dynamic features require continuous analysis over time frames, which could lead to higher resource requirements and slower inference speeds. Additionally, dynamic features might introduce noise or variability that could impact model robustness if not properly handled through preprocessing or feature engineering techniques.

How could self-distillation methods impact training efficiency in other deep learning applications

Self-distillation methods have the potential to significantly impact training efficiency in other deep learning applications by improving feature representation without increasing computational resources. By leveraging self-distillation techniques, models can learn from their own internal representations at different stages of training, leading to enhanced generalization capabilities and reduced overfitting risks. This approach allows for more efficient knowledge transfer within the network architecture while maintaining model performance levels across various tasks or datasets.