用于年龄估计的掩码对比图表示学习
Masked Contrastive Graph Representation Learning for Age Estimation
AI总结：
针对年龄估计中图像冗余信息建模的局限性，本文提出MCGRL方法，结合CNN提取语义特征与掩码GCN捕获结构信息，并通过多损失函数优化特征表示，显著提升了年龄估计性能。
摘要：
人脸图像的年龄估计是一项重要任务，在视频监控和互联网访问控制等领域具有各种实际应用。虽然基于深度学习的年龄估计框架，例如卷积神经网络（CNN）、多层感知机（MLP）和transformers，已经展现出卓越的性能，但在对包含大量冗余信息的图像中的复杂或不规则对象进行建模时，它们存在局限性。为了解决这个问题，本文利用图表示学习在处理图像冗余信息方面的鲁棒性，提出了一种用于年龄估计的新型掩码对比图表示学习（MCGRL）方法。具体而言，我们的方法首先利用CNN提取图像的语义特征，然后将其划分为块，作为图中的节点。接着，我们使用掩码图卷积网络（GCN）来推导基于图像的节点表示，以捕获丰富的结构信息。最后，我们引入多个损失函数来探索结构信息与语义特征之间的互补关系，从而提升GCN的特征表示能力。在真实世界人脸图像数据集上的实验结果证明了我们提出的方法优于其他最先进的年龄估计方法。
英文摘要：
Age estimation of face images is a crucial task with various practical applications in areas such as video surveillance and Internet access control. While deep learning-based age estimation frameworks, e.g., convolutional neural network (CNN), multi-layer perceptrons (MLP), and transformers have shown remarkable performance, they have limitations when modelling complex or irregular objects in an image that contains a large amount of redundant information. To address this issue, this paper utilizes the robustness property of graph representation learning in dealing with image redundancy information and proposes a novel Masked Contrastive Graph Representation Learning (MCGRL) method for age estimation. Specifically, our approach first leverages CNN to extract semantic features of the image, which are then partitioned into patches that serve as nodes in the graph. Then, we use a masked graph convolutional network (GCN) to derive image-based node representations that capture rich structural information. Finally, we incorporate multiple losses to explore the complementary relationship between structural information and semantic features, which improves the feature representation capability of GCN. Experimental results on real-world face image datasets demonstrate the superiority of our proposed method over other state-of-the-art age estimation approaches.
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。
