Cargando…
MsFcNET: Multi-scale Feature-Crossing Attention Network for Multi-field Sparse Data
Feature engineering usually needs to excavate dense-and-implicit cross features from multi-filed sparse data. Recently, many state-of-the-art models have been proposed to achieve low-order and high-order feature interactions. However, most of them ignore the importance of cross features and fail to...
Autores principales: | Xie, Zhifeng, Zhang, Wenling, Ding, Huiming, Ma, Lizhuang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206309/ http://dx.doi.org/10.1007/978-3-030-47426-3_12 |
Ejemplares similares
-
Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution
por: Zeng, Xueqiang, et al.
Publicado: (2023) -
GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention
por: Sharma, Udit, et al.
Publicado: (2021) -
MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation
por: Yan, Haitao, et al.
Publicado: (2022) -
Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation
por: Dayananda, Chaitra, et al.
Publicado: (2021) -
AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network
por: Fu, Chenghao, et al.
Publicado: (2023)