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 refine salient object details and an effective edge preservation loss to guide ...
这个模型主要由两部分组成:Feature Pyramid Attention(FPA)和 Global Attention Upsample(GAU) 其中FPA和deeplab里面的Spatial Pyramid Pooling很相似 FPA.png 全局注意力上采样模块 (Global Attention Upsample,GAU),对低层次特征执行 3×3 的卷积操作,以减少 CNN 特征图的通道数。从高层次特征生成的全局上下文信息依次...
speech resampling detection; feature pyramid network; SE attention; robustness1. Introduction In the rapidly evolving field of digital speech, particularly in the context of digital avatars, the ease of audio editing and modification through software (such as Audacity 3.4.2 [1], developed by ...
from attention import * from bilinear_upsampling import BilinearUpsampling class BatchNorm(BatchNormalization): def call(self, inputs, training=None): return super(self.__class__, self).call(inputs, training=True) class Copy(Layer): def call(self, inputs, **kwargs): copy = tf.ident...
Pyramid Feature Attention Network for Saliency detection Ting Zhao, Xiangqian Wu School of Computer Science and Technology, Harbin Institute of Technology 17S003073@stu.hit.edu.cn, xqwu@hit.edu.cn Abstract Saliency detection is one of the basic challenges in com- puter vision. ...
Feature Pyramid Attention(FPA)模块,可以在基于FCN的像素预测框架中嵌入不同尺度的上下文特征信息 Global Attention Upsample,用于融合不同level的信息,作用类似于refineNet中的Chained Residual Pooling 在VOC2012和cityscapes上的表现也不错。 目前的主干网络是ResNet101,不知换个轻量级网络效果是否依旧。
code and model of Pyramid Feature Selective Network for Saliency detection - CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection
第一种为DRN(Dilated Residual Network)形式,移除下采样层,使用具有不同空洞率的空洞卷积来有效地捕获远程信息(即语义上下文)而不降低空间分辨率 [4]。 另一种为FPN(Feature Pyramid Network)形式,基于 ConvNet [2] 的默认自下而上路径构建自上而下的特征金字塔。更具体地说,(更高级别)空间上较粗糙的特征图在...
论文阅读:ResNeSt: Split-Attention Networks 1、论文总述本篇论文在投稿阶段就在知乎上引发了广泛讨论,争议很多,这个争议我觉得不仅仅是ResNeSt特有的,而是现在的好多学术论文都有的问题,争议点就在于:文中提出的模型在数据集上提… 贾小树发表于论文阅读 【论文笔记 9】HAN:Hierarchical Attention Networks 论文标题:...
To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features. First, we design Context-aware Pyramid Feature Extraction (CPFE) module for multi-scale high-level feature maps to capture rich context ...