arXiv:2408.08544v1 Announce Type: cross
Abstract: Sign language serves as the primary meaning of communication for the deaf-mute community. Different from spoken language, it commonly conveys information by the collaboration of manual features, i.e., hand gestures and body movements, and non-manual features, i.e., facial expressions and mouth cues. To facilitate communication between the deaf-mute and hearing people, a series of sign language understanding (SLU) tasks have been studied in recent years, including isolated/continuous sign language recognition (ISLR/CSLR), gloss-free sign language translation (GF-SLT) and sign language retrieval (SL-RT). Sign language recognition and translation aims to understand the semantic meaning conveyed by sign languages from gloss-level and sentence-level, respectively. In contrast, SL-RT focuses on retrieving sign videos or corresponding texts from a closed-set under the query-by-example search paradigm. These tasks investigate sign language topics from diverse perspectives and raise challenges in learning effective representation of sign language videos. To advance the development of sign language understanding, exploring a generalized model that is applicable across various SLU tasks is a profound research direction.
Advances in Sign Language Understanding: A Multi-disciplinary Perspective
Sign language serves as the primary means of communication for the deaf-mute community, conveying information through a combination of manual and non-manual features such as hand gestures, body movements, facial expressions, and mouth cues. In recent years, there has been a growing interest in developing sign language understanding (SLU) systems to facilitate communication between the deaf-mute and hearing individuals.
The Multi-disciplinary Nature of Sign Language Understanding
Sign language understanding involves multiple disciplines, including linguistics, computer vision, machine learning, and multimedia information systems. Linguistics provides insights into the structure and grammar of sign languages, helping researchers design effective representations for capturing the semantic meaning conveyed by sign languages.
Computer vision and machine learning techniques are essential for analyzing the visual features of sign language videos. These techniques enable the extraction of hand gestures, body movements, and facial expressions from video sequences, which are then used for recognition, translation, or retrieval tasks. Additionally, these disciplines contribute to the development of computer vision algorithms capable of understanding sign language in real-time or near real-time scenarios.
Multimedia information systems play a crucial role in sign language understanding, providing platforms for creating, storing, and retrieving sign language videos. These systems also enable the integration of additional multimedia modalities, such as text or audio, to enhance the comprehension of sign language content. Furthermore, multimedia information systems enable the creation of sign language databases, which are essential for training and evaluating SLU models.
Sign Language Understanding Tasks
Several sign language understanding tasks have been studied in recent years, each addressing different aspects of sign language communication:
- Isolated/Continuous Sign Language Recognition (ISLR/CSLR): These tasks focus on recognizing hand gestures and body movements in isolated signs or continuous sign sequences. By analyzing the visual features extracted from sign language videos, ISLR and CSLR aim to understand the meaning conveyed by individual signs or complete sentences.
- Gloss-free Sign Language Translation (GF-SLT): Unlike traditional sign language translation, which maps individual signs to spoken language words, GF-SLT aims to directly translate sign language videos into the target language without relying on gloss-level annotations. This task requires the development of advanced machine learning models capable of handling the structural complexity of sign languages.
- Sign Language Retrieval (SL-RT): SL-RT focuses on retrieving sign videos or corresponding texts from a closed-set based on examples provided by the user. This task enables efficient access to sign language content, allowing individuals to search for specific signs or sentences in sign language databases.
Challenges and Future Directions
Developing a generalized model that is applicable across various sign language understanding tasks poses significant challenges. One key challenge is designing effective representations that capture the rich semantic information present in sign language videos. This requires incorporating both manual and non-manual features, as well as considering the temporal dynamics of sign language.
Another challenge is the lack of large-scale annotated sign language datasets. Training deep learning models for sign language understanding often requires vast amounts of labeled data. However, the creation of such datasets is time-consuming and requires expert annotation. Addressing this challenge requires innovative solutions, such as leveraging weakly supervised or unsupervised learning methods for sign language understanding.
In conclusion, sign language understanding is a multi-disciplinary field that combines knowledge from linguistics, computer vision, machine learning, and multimedia information systems. Advancing the state-of-the-art in sign language understanding requires collaboration and contributions from these diverse disciplines. By addressing the challenges and exploring new directions, we can pave the way for improved communication and inclusivity for the deaf-mute community.