RFA:Receptive-Field Attention 34.vAttention vAttention 35.PyramindAttention Hierarchical Attention Pyramid Attention HAM:Hybrid Attention Modue 总结 前言 1.Attention Attention 题目:Attention Is All You Need 名称:Attention是你所需要 论文:arxiv.org/abs/1706.0376 代码: 2.SelfAttention RPR Self-Attention...
Firstly, a context model is exploited to increase the receptive fields at the beginning of the network. Secondly, stacked pyramid feature attention modules and feature fusion simultaneously selectively integrate the contextual information and enable spatial details, thus enhancing the capac...
We use global CA [26] to weight deep features using multiple receptive fields to capture highly discriminative channel features. Channel-level statistics are first generated by Equation (4) to obtain a feature map of size 1 × 1 × C with global sensory fields, aggregating global contextual ...
In this paper, we propose a novel saliency detection method, which contains: a context-aware pyramid feature extraction module and a channel-wise attention module to capture context-aware multi-scale multi-receptive-field high-level features a spatial attention module for low-level feature maps to ...
[25] recommend fine-tuning the receptive fields in CNN architectures to improve recognition capabilities. Additionally, attention mechanisms have gained traction for highlighting key features in input signals, thereby enhancing model performance 26, 27, 16, 28-30. To better represent audio-visual ...
3. Pyramid Feature Attention Network In this paper, we propose a novel saliency detection method, which contains a context-aware pyramid feature extraction module and a channel-wise attention module to capture context-aware multi-scale multi-receptive-field high-level features, a s...
In this study, we have proposed an extended version of U-Net named multi-level attention dilated residual neural network (MADR-Net) for the segmentation of medical images. By replacing the U-Net architecture, high-level features were extracted from multiple receptive fields and these connections ...
This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and...
2.1. Backbone network It is important for semantic segmentation models to preserve enough spatial information while offering a sufficiently large receptive field. Spatial information ensures detail sharpness in the segmentation, and a large receptive field ensures the correctness of the assigned category. ...
A recent work [5] also pointed out that simply stacking convolution layers is inefficient in increasing effective receptive fields to capture enough contextual information. Inspired by the success of attention modules [6] in natural language processing (NLP), the relation network [7] was proposed ...