Report on Current Developments in Sign Language Contextual Processing with Embedding from LLMs

General Direction of the Field

The field of sign language recognition (SLR) and translation (SLT) is currently moving towards more context-aware and multi-modal approaches, leveraging advancements in large language models (LLMs) and 3D data processing. Researchers are increasingly focusing on integrating contextual information from dialogues to enhance the accuracy and robustness of SLR and SLT systems. This shift is driven by the recognition that traditional vision-based methods, which often rely solely on visual cues, struggle with the complexity and variability of sign language in real-world dialogue scenarios.

One of the key innovations in this area is the use of multi-modal encoders that combine visual data with contextual information from dialogues. This approach allows for more nuanced understanding of sign language, particularly in dynamic and interactive settings. Additionally, the incorporation of LLMs for fine-tuning in SLT tasks is proving to be a significant advancement, enabling more accurate and contextually relevant translations.

Another notable trend is the development and utilization of 3D datasets for sign language processing. These datasets capture not only hand movements but also facial expressions and body postures, providing a more comprehensive representation of sign language. The integration of 3D data with existing benchmarks and linguistic resources is facilitating deeper analyses and more robust models.

Furthermore, there is a growing emphasis on cost-effective data generation techniques, such as concatenating short video clips to create larger, more diverse datasets. This approach addresses the scarcity of labeled data, particularly for less commonly studied sign languages, and opens up new possibilities for training more inclusive and accurate translation models.

Noteworthy Developments

• SCOPE Framework: Introduces a novel context-aware vision-based SLR and SLT framework that leverages dialogue contexts and fine-tunes LLMs, achieving state-of-the-art performance.
• 3D-LEX v1.0 Dataset: Presents an efficient 3D motion capture approach and dataset that enhances gloss recognition accuracy and supports 3D-aware sign language processing.
• Less is More Approach: Proposes a cost-effective method for generating sign language content by concatenating short clips, significantly improving translation model performance with limited resources.
• 1DCNNTrans Model: Demonstrates superior performance in sign language recognition tasks, particularly for classes with varying complexity, enhancing inclusiveness in public services.