P-Age: 用于鲁棒时空表观年龄分类的Pexels数据集
P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification
AI总结：
本研究针对年龄估计中的遮挡、低分辨率等挑战，提出基于双流架构的AgeFormer视频年龄分类方法，并构建P-Age视频数据集，在多类复杂场景和数据集上取得优于传统人脸方法的效果。
摘要：
年龄估计是一项具有挑战性的任务，拥有众多应用场景。本文提出了年龄分类的新方向，采用基于视频的模型来应对遮挡、低分辨率和光照条件等挑战。为解决这些难题，我们提出了AgeFormer算法，该算法利用整个人体动态的时空信息，在年龄分类任务中优于基于人脸的方法。我们提出的新型双流架构以TimeSformer和EfficientNet作为骨干网络，可有效捕捉面部和身体动态信息，实现视频中高效准确的年龄估计。此外，为填补现实场景下视频年龄预测的空白，我们构建了名为Pexels Age（P-Age）的视频年龄分类数据集。所提方法相比现有基于人脸的年龄估计方法取得了更优结果，且在人脸高度遮挡、模糊或戴口罩的场景下完成了评估。该方法还在Charades、Smarthome、Thumos-14等多种具有挑战性的视频数据集上进行了交叉测试。
英文摘要：
Age estimation is a challenging task that has numerous applications. In this paper, we propose a new direction for age classification that utilizes a video-based model to address challenges such as occlusions, low-resolution, and lighting conditions. To address these challenges, we propose AgeFormer which utilizes spatio-temporal information on the dynamics of the entire body dominating face-based methods for age classification. Our novel two-stream architecture uses TimeSformer and EfficientNet as backbones, to effectively capture both facial and body dynamics information for efficient and accurate age estimation in videos. Furthermore, to fill the gap in predicting age in real-world situations from videos, we construct a video dataset called Pexels Age (P-Age) for age classification. The proposed method achieves superior results compared to existing face-based age estimation methods and is evaluated in situations where the face is highly occluded, blurred, or masked. The method is also cross-tested on a variety of challenging video datasets such as Charades, Smarthome, and Thumos-14.
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。
