betekintés - Computer Vision - # Continuous Sign Language Recognition

Constructing a Large-Scale Chinese Continuous Sign Language Dataset for Complex Real-World Environments and Proposing a Time-Frequency Network for Efficient Recognition

Q: How can the CE-CSL dataset be further expanded to include more diverse real-world scenarios and sign language variations?

To further expand the CE-CSL dataset and enhance its diversity, several strategies can be implemented. First, increasing the number of sign language performers from various regions and backgrounds can introduce a wider array of sign language variations, including regional dialects and unique signing styles. This would ensure that the dataset captures the rich linguistic diversity present in the Chinese sign language community. Second, the dataset can be expanded by incorporating additional real-world scenarios that reflect different contexts in which sign language is used. This could include environments such as educational settings, workplaces, public transportation, and social gatherings. By capturing sign language in these varied contexts, the dataset would better represent the complexities of everyday communication. Third, utilizing crowdsourcing methods to gather video data from a broader audience can help in collecting more spontaneous and natural sign language interactions. This approach would allow for the inclusion of informal settings and unscripted conversations, which are often absent in controlled environments. Lastly, integrating multimodal data, such as audio and text annotations, can provide richer context for each sign language video. This would not only enhance the dataset's usability for training deep learning models but also facilitate research into the interplay between sign language and other forms of communication.

Q: What other deep learning architectures or feature extraction techniques could be explored to enhance the performance of continuous sign language recognition in complex environments?

To enhance the performance of continuous sign language recognition (CSLR) in complex environments, several advanced deep learning architectures and feature extraction techniques can be explored. One promising approach is the use of Transformer-based models, which have shown significant success in natural language processing tasks. Transformers can capture long-range dependencies in sign language sequences, making them suitable for recognizing complex gestures and movements. Another potential architecture is the use of Graph Neural Networks (GNNs), which can model the spatial relationships between different body parts during sign language expression. By treating the human body as a graph, GNNs can effectively capture the dynamics of hand movements and facial expressions, which are crucial for accurate sign language recognition. Additionally, exploring hybrid models that combine Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks can improve feature extraction from both spatial and temporal dimensions. This combination allows for the effective modeling of both the visual features of sign language and the temporal sequences of gestures. Furthermore, employing attention mechanisms can enhance the model's focus on relevant parts of the input video, improving recognition accuracy in noisy or cluttered backgrounds. Techniques such as spatial attention and temporal attention can help the model prioritize significant movements and gestures, leading to better performance in complex environments. Lastly, leveraging unsupervised or semi-supervised learning techniques can help in utilizing unlabeled data to improve model robustness. By training on a larger pool of diverse data, the model can generalize better to real-world scenarios, thus enhancing its performance in continuous sign language recognition tasks.

Alapfogalmak

This study constructs a large-scale Chinese Continuous Sign Language dataset (CE-CSL) oriented towards practical application environments, featuring diverse real-world backgrounds, and proposes a Time-Frequency Network (TFNet) model that achieves efficient and accurate continuous sign language recognition by extracting sequence features in both the temporal and frequency domains.

Kivonat

The paper presents the construction of a new Chinese Continuous Sign Language (CSL) dataset called CE-CSL, which aims to address the limitations of existing datasets that are primarily collected in laboratory environments or television program recordings. The CE-CSL dataset encompasses 5,988 continuous CSL video clips collected from daily life scenes, featuring more than 70 different complex backgrounds to ensure representativeness and generalization capability.

To tackle the impact of complex backgrounds on continuous sign language recognition (CSLR) performance, the authors propose a Time-Frequency Network (TFNet) model. This model first extracts frame-level features using a CNN, and then utilizes both temporal and spectral information to separately derive sequence features before fusion, aiming to achieve efficient and accurate CSLR.

Experimental results demonstrate that the proposed TFNet model achieves significant performance improvements on the CE-CSL dataset, validating its effectiveness under complex background conditions. Additionally, the TFNet model has also yielded highly competitive results when applied to three publicly available CSL datasets, indicating its high generality and robustness across different datasets.

The key highlights of the study include:

Construction of a large-scale Chinese Continuous Sign Language dataset (CE-CSL) that reflects the complexity of real-world environments, with diverse backgrounds and natural lighting conditions.
Proposal of a Time-Frequency Network (TFNet) model that extracts sequence features in both the temporal and frequency domains to achieve efficient and accurate continuous sign language recognition.
Extensive experiments on the CE-CSL dataset and three other publicly available CSL datasets, demonstrating the superior performance and strong generalization ability of the TFNet model.

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Átírás mesterséges intelligenciával

Hivatkozások generálása

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a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

The CE-CSL dataset contains 5,988 continuous CSL video clips collected from daily life scenes, featuring more than 70 different complex backgrounds.
The dataset is divided into 4,973 training videos, 515 validation videos, and 500 test videos.
The CE-CSL dataset covers 3,515 Chinese words, representing a wide range of daily communication needs.

Idézetek

"To overcome the limitations of existing continuous sign language datasets, particularly their disconnection from real-life scenarios, this study constructs a CSL dataset oriented towards practical application environments."
"We propose a TFNet model for continuous sign language recognition. This model, by leveraging information from both the temporal and frequency domains to extract sequence features, achieves efficient and accurate semantic parsing, significantly improving the accuracy of CSLR in complex environments."

Főbb Kivonatok

A Chinese Continuous Sign Language Dataset Based on Complex Environments

by Qidan Zhu, J... : arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.11960.pdf

A Chinese Continuous Sign Language Dataset Based on Complex Environments

Mélyebb kérdések

How can the CE-CSL dataset be further expanded to include more diverse real-world scenarios and sign language variations?

To further expand the CE-CSL dataset and enhance its diversity, several strategies can be implemented. First, increasing the number of sign language performers from various regions and backgrounds can introduce a wider array of sign language variations, including regional dialects and unique signing styles. This would ensure that the dataset captures the rich linguistic diversity present in the Chinese sign language community.
Second, the dataset can be expanded by incorporating additional real-world scenarios that reflect different contexts in which sign language is used. This could include environments such as educational settings, workplaces, public transportation, and social gatherings. By capturing sign language in these varied contexts, the dataset would better represent the complexities of everyday communication.
Third, utilizing crowdsourcing methods to gather video data from a broader audience can help in collecting more spontaneous and natural sign language interactions. This approach would allow for the inclusion of informal settings and unscripted conversations, which are often absent in controlled environments.
Lastly, integrating multimodal data, such as audio and text annotations, can provide richer context for each sign language video. This would not only enhance the dataset's usability for training deep learning models but also facilitate research into the interplay between sign language and other forms of communication.

What other deep learning architectures or feature extraction techniques could be explored to enhance the performance of continuous sign language recognition in complex environments?

To enhance the performance of continuous sign language recognition (CSLR) in complex environments, several advanced deep learning architectures and feature extraction techniques can be explored. One promising approach is the use of Transformer-based models, which have shown significant success in natural language processing tasks. Transformers can capture long-range dependencies in sign language sequences, making them suitable for recognizing complex gestures and movements.
Another potential architecture is the use of Graph Neural Networks (GNNs), which can model the spatial relationships between different body parts during sign language expression. By treating the human body as a graph, GNNs can effectively capture the dynamics of hand movements and facial expressions, which are crucial for accurate sign language recognition.
Additionally, exploring hybrid models that combine Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks can improve feature extraction from both spatial and temporal dimensions. This combination allows for the effective modeling of both the visual features of sign language and the temporal sequences of gestures.
Furthermore, employing attention mechanisms can enhance the model's focus on relevant parts of the input video, improving recognition accuracy in noisy or cluttered backgrounds. Techniques such as spatial attention and temporal attention can help the model prioritize significant movements and gestures, leading to better performance in complex environments.
Lastly, leveraging unsupervised or semi-supervised learning techniques can help in utilizing unlabeled data to improve model robustness. By training on a larger pool of diverse data, the model can generalize better to real-world scenarios, thus enhancing its performance in continuous sign language recognition tasks.

How can the proposed TFNet model be adapted or extended to enable real-time continuous sign language translation, bridging the communication gap between the deaf and hearing communities?

To adapt the proposed TFNet model for real-time continuous sign language translation, several modifications and enhancements can be implemented. First, optimizing the model for speed and efficiency is crucial. This can be achieved by reducing the model's complexity through techniques such as model pruning, quantization, or using lightweight architectures like MobileNet or EfficientNet. These approaches can significantly decrease inference time, making the model suitable for real-time applications.
Second, integrating a streaming data processing pipeline would allow the model to process video frames in real-time. This could involve using a sliding window approach, where the model continuously analyzes incoming frames and updates its predictions dynamically. Implementing a buffer system can help manage the input stream, ensuring that the model maintains context while processing each frame.
Third, enhancing the model's ability to handle variations in signing speed and style is essential for real-time translation. This can be achieved by incorporating adaptive learning techniques that allow the model to adjust its parameters based on the speed and rhythm of the sign language being performed. Additionally, training the model on diverse datasets that include various signing speeds and styles can improve its robustness.
Moreover, incorporating feedback mechanisms, such as user corrections or confirmations, can help refine the model's predictions in real-time. This interactive approach would allow users to provide input on the accuracy of the translation, enabling the model to learn and adapt continuously.
Lastly, developing a user-friendly interface that facilitates seamless communication between deaf and hearing individuals is vital. This could involve integrating the TFNet model into mobile applications or wearable devices, allowing for easy access and interaction in everyday situations. By focusing on user experience and accessibility, the adapted TFNet model can effectively bridge the communication gap between the deaf and hearing communities, promoting inclusivity and understanding.