ViBED-Net：基于面部感知和场景感知时空线索的视频参与度检测网络
ViBED-Net: Video Based Engagement Detection Network Using Face-Aware and Scene-Aware Spatiotemporal Cues
AI总结：
ViBED-Net提出双流深度学习框架，结合面部与场景时空线索，在DAiSEE数据集上以73.43%准确率超越现有方法，实现高效参与度检测。
摘要：
在线学习环境中的参与度检测对于改善学生成绩和个性化教学至关重要。我们提出了ViBED-Net（基于视频的参与度检测网络），一种新颖的深度学习框架，旨在通过双流架构从视频数据中评估学生参与度。ViBED-Net通过处理面部裁剪图像和整个视频帧，利用EfficientNetV2进行空间特征提取，同时捕捉面部表情和全场景上下文。这些特征随后通过两种时间建模策略进行分析：长短期记忆（LSTM）网络和Transformer编码器。我们的模型在DAiSEE数据集上进行了评估，该数据集是电子学习中情感状态识别的大规模基准。为了增强代表性不足的参与度类别的性能，我们应用了针对性的数据增强技术。在测试的变体中，使用LSTM的ViBED-Net达到了73.43%的准确率，优于现有的最先进方法。ViBED-Net证明，结合面部感知和场景感知的时空线索能显著提高参与度检测的准确性。其模块化设计使其能够灵活应用于教育、用户体验研究和内容个性化等领域。这项工作通过提供可扩展、高性能的解决方案，推进了基于视频的情感计算在真实世界参与度分析中的应用。该项目的源代码可在https://github.com/prateek-gothwal/ViBED-Net获取。
英文摘要：
Engagement detection in online learning environments is vital for improving student outcomes and personalizing instruction. We present ViBED-Net (Video-Based Engagement Detection Network), a novel deep learning framework designed to assess student engagement from video data using a dual-stream architecture. ViBED-Net captures both facial expressions and full-scene context by processing facial crops and entire video frames through EfficientNetV2 for spatial feature extraction. These features are then analyzed over time using two temporal modeling strategies: Long Short-Term Memory (LSTM) networks and Transformer encoders. Our model is evaluated on the DAiSEE dataset, a large-scale benchmark for affective state recognition in e-learning. To enhance performance on underrepresented engagement classes, we apply targeted data augmentation techniques. Among the tested variants, ViBED-Net with LSTM achieves 73.43\% accuracy, outperforming existing state-of-the-art approaches. ViBED-Net demonstrates that combining face-aware and scene-aware spatiotemporal cues significantly improves engagement detection accuracy. Its modular design allows flexibility for application across education, user experience research, and content personalization. This work advances video-based affective computing by offering a scalable, high-performing solution for real-world engagement analysis. The source code for this project is available on https://github.com/prateek-gothwal/ViBED-Net .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
