We hypothesize that optimizing the convex combination of the features is preferred to modeling their correlations by computationally heavy multi-head self attention. We propose Lightweight Attentional Feature Fusion (LAFF). LAFF performs feature fusion at both early and late stages and at both video ...
为解决上述问题, 本文提出一种用于实时语义分割的轻量化卷积注意力特征融合网络(lightweight convolutional attention feature fusion networks, LCANet). 该网络采用经典的编码器-解码器架构, 编码器的基础单元是空洞MobileNet模块(dilated MobileNet block, DMB), 其通过引入空洞卷积层, 在不增加参数量的情况下获得额外...
attentionfeature fusionWith the development of deep learning technology, more and more researchers are interested in ear recognition. Human ear recognition is a biometric identification technology based on human ear feature information and it is often used for authentication and intelligent monitoring field...
39 proposed the Convolutional Triplet Attention Module, learning the relationship between the three dimensions through a three-branch attention mechanism. Moreover, the attention mechanism also shows great potential in feature fusion. Liu et al.40 introduced Feature Pyramid Encoding Network that fuses ...
In this article, we address a network architecture search (NAS)-guided lightweight spectral-spatial attention feature fusion network (LMAFN) for HSI classification. The overall architecture of the proposed network is guided by several conclusions of NAS, which achieves fewer parameters and lower ...
LFFNet: lightweight feature-enhanced fusion network for real-time semantic segmentation of road scenes Article05 March 2024 References Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and...
This paper proposes a dual-track parallel lightweight and precise 1D_2DIFCNN model, which achieves accurate, stable, and fast fault diagnosis through its one-dimensional and two-dimensional parallel dual-channel architecture, convolutional attention, and feature fusion strategy. Two different datasets ...
2023, Information Fusion Citation Excerpt : Similar to channel attention, the spatial attention module is used in [82,83] to improve the SR models based on capturing long-distance spatial contextual information. Many attention-based methods based on the multi-scale feature extraction [84–88] can...
We enhance feature extraction efficiencyby combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive...
Specifically, in order to extract as much information as possible with low weights, MWIB utilizes a standard convolution and three lightweight residual attention blocks (RABs) to achieve multi-scale feature fusion. Each RAB utilizes two lightweight blocks (LWBs) and an enhanced channel attention ...