Diversity-Aware Sign Language Production
Abstract
In the realm of sign language interpretation, diversity and inclusivity are paramount. This research delves into the groundbreaking concept of diversity-aware sign language production, introducing a novel approach to generate alternative images of signers with varying attributes (e.g., gender, skin color) while preserving the original pose. This innovative method empowers communication and fosters understanding across diverse audiences.
Methods
Our proposed model leverages a sophisticated combination of techniques to achieve diversity-aware sign language production:
* Variational Inference Extension: We extend variational inference to seamlessly incorporate pose information and attribute conditioning, enabling the generation of diverse images with precise control.
* UNet Generator: Employing a UNet architecture, our generator excels in preserving the spatial integrity of the input pose, ensuring accurate representation of the sign language gestures.
* Visual Feature Inclusion: To control the appearance and style of the generated images, we incorporate visual features extracted from the variational inference process, allowing for fine-tuned customization.
* Separate Decoders: Each body part in the sign language gesture is generated by a distinct decoder, enabling precise control over the individual components and enhancing the overall realism of the produced images.
Evaluation
To assess the efficacy of our model, we conducted rigorous evaluations on the extensive SMILE II dataset, a comprehensive resource for sign language analysis. Our model was subjected to a battery of metrics, including:
* Diversity: We measured the model’s ability to generate diverse images with varying attributes while maintaining the same pose.
* Per-pixel Image Quality: The quality of the generated images was meticulously evaluated to ensure high fidelity and visual appeal.
* Pose Estimation: The accuracy of the generated images in terms of capturing the intended sign language pose was thoroughly assessed.
* Non-manual Feature Reproduction: We examined the model’s ability to reproduce non-manual features, such as facial expressions and body posture, which are crucial for conveying emotions and nuances in sign language.
Results
Our model outshined existing baselines across all evaluation metrics, demonstrating its superiority in diversity-aware sign language production. It excelled in generating diverse images with varying attributes while maintaining the intended pose, achieving high image quality, and accurately reproducing non-manual features. These findings underscore the model’s potential to revolutionize sign language interpretation, breaking down barriers to communication and fostering inclusivity.
Diversity-Aware Sign Language Production and Federated Learning for Misbehavior Detection
Diversity-Aware Sign Language Production
Abstract
In this groundbreaking research, we present a novel approach to diversity-aware sign language production. Our method generates diverse images depicting signers with varying attributes (such as gender and skin color) while preserving the same pose.
Methods
We ingeniously extend variational inference to incorporate pose information and attribute conditioning. Our model employs a UNet architecture, ensuring spatial preservation of the input pose. Visual features from variational inference are integrated to control appearance and style, while separate decoders generate each body part.
Evaluation
Rigorous evaluation on the SMILE II dataset demonstrates the superiority of our model. It outperforms baselines in diversity, image quality, pose estimation, and non-manual feature reproduction.
Federated Learning for Misbehavior Detection
Abstract
We propose an innovative unsupervised federated learning (FL) approach for misbehavior detection in vehicular environments. Our solution leverages FL for collaborative model training while maintaining data privacy.
Methods
Our model utilizes Gaussian Mixture Models (GMM) to identify potential misbehavior and Variational Autoencoders (VAE) to model normal behavior. Pre-training with Restricted Boltzmann Machines (RBM) enhances convergence. The Fedplus aggregation function further improves performance.
Evaluation
Evaluated on the VeReMi dataset, our model achieves an impressive performance of over 80%. It outperforms supervised baseline approaches, demonstrating its effectiveness in misbehavior detection.
Conclusion
Our research makes significant contributions to the fields of sign language production and misbehavior detection. By promoting diversity and enhancing safety, our models have the potential to empower individuals and improve society.