Specifically, we propose a Multi-Scale Adaptive Spatial Attention Gate (MASAG), which dynamically adjusts the receptive field (Local and Global contextual information) to ensure that spatially relevant features are selectively highlighted while minimizing background distractions. Extensive evaluations ...
Taking the encoder–decoder architecture as the backbone network, a multi-scale attention fusion network, named MAF-Net, is proposed for automatic surgical instrument segmentation, which introduces the residual dense module, AFM module, and MSAC module to improve segmentation accuracy as more as possib...
By adding multi-scale layers and dense layers, the network can capture the sequence features and ensemble different time-scale attention information. Meanwhile it is an end-to-end network combining the feature extraction methods and RUL models only by pre-training the RBM model so it is more ...
2019ICCVDepth-induced Multi-scale Recurrent Attention Network for Saliency Detection Wei Ji, Jingjing Li, Miao Zhang, Huchuan LuPaper/Code Ext-22 2019CVPRContrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection Jia-Xing Zhao, Ming-Ming Cheng, et al.Paper/Code ...
Multi-head attention arbitration network The extracted features by multi-scale CNN and bi-LSTM are contacted into a vector. And the feature vector is fed into the arbitration network for their weights redistribution. The arbitration network depends mainly on multi-head attention mechanism to efficientl...
提出一种新的多模态数据融合方法,即PCAG(Pre-gating and Contextual Attention Gate),以解决现有跨模态交互学习中的噪声问题和不确定性问题。 PCAG包含两个关键机制:Pre-gating和Contextual Attention Gate (CAG)。Pre-gating在跨注意力之前直接控制跨模态交互的生成,而CAG则在跨注意力之后,利用上下文信息来评估生成的...
Notes:shared feature attention(spatial attention) masks relevant backbone features for each task, allowing it to learn a generic representation; a novelMulti-Scale Attentionhead allows the network to better combine per-task features from different scales when making the final prediction. ...
Multi-head attention arbitration network The extracted features by multi-scale CNN and bi-LSTM are contacted into a vector. And the feature vector is fed into the arbitration network for their weights redistribution. The arbitration network depends mainly on multi-head attention mechanism to efficientl...
MAN (Wang et al., 2024) also introduced large kernel convolutional attention and multi-scale large kernel convolution based on GoogleNet (Szegedy et al., 2015) and ConvNeXt (Liu et al., 2022), and its simplified gated spatial attention unit (GSAU) was designed by a Simple Gate(SG) (...
Secondly, Multi-Scale Recurrent Convolutional Neural Network (RCNN) is employed to further extract contextual information from news texts. Self-attention is introduced to calculate attention scores between news articles, allowing for mutual influence between news features. The establishment of connections ...