To address this issue, this study adopted a block-based processing strategy and constructed a lightweight multilevel feature-fusion (FF) convolutional neural network for the feature representation and discrimination of built-up areas in HR images. The proposed network consists of three feat...
Therefore, we propose a new lightweight edge-guided multilevel feature fusion camouflaged object detection network, codenamed as LEMFNet. Initially, we adopt a lightweight CNN network model for feature extraction to reduce model complexity. Subsequently, we introduced a neighborhood feature association ...
which often suffers from the sparse data and bad performance. In this paper, we propose a new pre-trained multilevel fusion network based on Vision-conditioned reasoning and Bilinear attentions for Med-VQA (VB
Peng, Z.: Multi-level spatial-temporal fusion neural network for traffic flow prediction. Clust. Comput. (2024). https://doi.org/10.1007/s10586-024-04296-8 Article MATH Google Scholar Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-gcn...
Based on the application of multilevel local feature coding in music genre recognition, the fused music features are input into the improved deep learning network, and the music genre style recognition model is constructed to realize music genre recognition. The specific steps are as follows. ...
current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications. In this paper, we focus on the task of building the HD maps in both short ranges, i.e., within 30 m,...
Based on an end-to-end convolutional neural network (CNN), the paper proposed a road extraction model combining spatial attention mechanism, global information perception, and feature fusion module. It can extract road information with de
We orchestrated two feature encoders predicated upon the neural network architecture to procure image features, denoted as fq and fr respectively. During the training process, θ was used in dynamic update mode: θr←mθr + (1 − m) θq. 2.5. Multi-Dimensional Fusion Loss Function In ...
finger vein features; inner knuckle print features; multimodal recognition; convolutional neural network; feature fusion1. Introduction Biometric recognition aims to distinguish individuals based on human physiological and behavioral characteristics and is widely applied in Internet-of-Things security [1], ...
Hyperspectral images (HSIs), acquired as a 3D data set, contain spectral and spatial information that is important for ground–object recognition. A 3D convolutional neural network (3DCNN) could therefore be more suitable than a 2D one for extracting mul