It is a challenge to effectively use the critical feature of multi-scale for finger veins. In this article, we extract multi-scale features via pyramid convolution. We propose scale attention, namely, the scale-
To address these problems, we propose a multi-scale attention network (MSA-Net) for image inpainting, in which a multi-scale attention group (MSAG) is presented to improve the performance of inpainting network. Here, several multi-scale attention units (MSAUs) are included in MSAG to catch th...
In order to improve the detection accuracy of the network, it proposes multi-scale feature fusion and attention mechanism net (MFANet) based on deep learning, which integrates pyramid module and channel attention mechanism effectively. Pyramid module is designed for feature fusion in the channel and...
The model uses the feature pyramid network (FPN) structure and a one-dimensional convolutional block attention module (1D-CBAM) for feature fusion to enhance the classification ability of the model. This model is used to extract sheep voiceprint features and combined with the proposed similarity ...
A complex roadside object detection model based on multi-scale feature pyramid network Article Open access 08 May 2025 PTCDet: advanced UAV imagery target detection Article Open access 09 November 2024 Introduction Object detection in road scene is one of the core problems of intelligent traffic...
SFAM聚合TUMs产生的多级多尺度特征,以构造一个多级特征金字塔。第一步,SFAM沿着channel维度将拥有相同scale的feature map进行拼接,这样得到的每个scale的特征都包含了多个level的信息。第二步,借鉴SENet的思想,加入channel-wise attention,以更好地捕捉有用的特征。SFAM的细节如下图所示: ...
multi-level multiscale features. In addition, SFAM aggregates the features into the multi-level feature pyramid through a scale-wise feature concatenation operation and an adaptive attention mechanism. More details about the three core modules and network configurations in M2Det are introduced in ...
multi-level multiscale features. In addition, SFAM aggregates the features into the multi-level feature pyramid through a scale-wise feature concatenation operation and an adaptive attention mechanism. More details about the three core modules and network configurations in M2Det are introduced in ...
In this paper, we first propose a multi-scale channel attention network with an adaptive feature fusion strategy (MSCAN-AFF) for face recognition (FR), which fuses the relevant feature channels and improves the network’s representational power. In FR, face alignment is performed independently ...
network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present ...