Abstract
In Neural Networks, there are various methods of feature fusion. Different strategies can significantly affect the effectiveness of feature representation, consequently influencing the ability of model to extract representative and discriminative features. In the field of face recognition, traditional feature fusion methods include feature concatenation and feature addition. Recently, various attention mechanism-based fusion strategies have emerged. However, we found that these methods primarily focus on the important features in the image, referred to as salient features in this paper, while neglecting another equally important set of features for image recognition tasks, which we term differential features. This may cause the model to overlook critical local differences when dealing with complex facial samples. Therefore, in this paper, we propose an efficient convolution module called MSConv (Multiplicative and Subtractive Convolution), designed to balance the learning of model about salient and differential features. Specifically, we employ multi-scale mixed convolution to capture both local and broader contextual information from face images, and then utilize Multiplication Operation (MO) and Subtraction Operation (SO) to extract salient and differential features, respectively. Experimental results demonstrate that by integrating both salient and differential features, MSConv outperforms models that only focus on salient features.
Abstract (translated)
在神经网络中，存在多种特征融合的方法。不同的策略会显著影响特征表示的有效性，从而影响模型提取代表性及区分性特征的能力。在人脸识别领域，传统的特征融合方法包括特征级联和特征相加。近期，基于注意力机制的融合策略也开始涌现。然而，我们发现这些方法主要关注图像中的重要特征（本文称为显著特征），而忽视了另一组同样重要的、用于图像识别任务的特征，我们将这组特征命名为差异性特征。这可能导致模型在处理复杂面部样本时忽略关键局部差异。因此，在本文中，我们提出了一种高效的卷积模块——MSConv（乘法和减法卷积），旨在平衡模型对显著性和差异性特征的学习。具体而言，我们采用多尺度混合卷积来捕捉人脸图像中的局部信息及更广泛的上下文，并通过乘法运算(MO)和减法运算(SO)分别提取显著性和差异性特征。实验结果表明，通过融合这两类特征，MSConv的表现优于仅关注显著性特征的模型。
URL
https://arxiv.org/abs/2503.06187
