Summary of Towards In-vehicle Multi-task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models, by Esmaeil Seraj and Walter Talamonti
Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models
by Esmaeil Seraj, Walter Talamonti
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of enhancing vehicle-driver interaction through facial attribute recognition in intelligent transportation systems. They investigate the potential of synthetic datasets and state-of-the-art vision foundation models to recognize facial attributes such as gaze plane, age, and facial expression. The authors utilize transfer learning techniques with pre-trained Vision Transformer (ViT) and Residual Network (ResNet) models to optimize performance when data is limited. They provide extensive post-evaluation analysis, investigating the effects of synthetic data distributions on model performance in in-distribution data and out-of-distribution inference. Their study reveals counter-intuitive findings, including the superior performance of ResNet over ViTs in their specific multi-task context. This work highlights the challenges and opportunities for enhancing the use of synthetic data and vision foundation models in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are working to make vehicles safer by better understanding how drivers react to different situations. They’re using computer vision techniques to recognize facial attributes like expression and gaze direction. The problem is that there aren’t many real-world datasets available, so they’re exploring the use of synthetic data instead. This allows them to train models on a large amount of fake data and then test how well they work in real-world scenarios. They compared two different types of models, called Vision Transformers and Residual Networks, and found that one of them worked better than the other when working with limited data. |
Keywords
* Artificial intelligence * Inference * Multi task * Residual network * Resnet * Synthetic data * Transfer learning * Vision transformer * Vit
