In this paper, a Pyramid Channel-based Feature Attention Network (PCFAN) is proposed for single image dehazing, which leverages complementarity among different level features in a pyramid manner with channel at
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 ...
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. R...
(2) Overwhelming amount of parameters in the deep neural network will lead to low efficiency. To address these two limitations, in this paper, we propose a Feature Pyramid Attention Network (FPANet). Specifically, the FPANet consists of three modules, namely the feature pyramid module, ...
FPN特征金字塔,缓解了上述问题,但是多尺度感知域直接缺少信息的沟通,而且这类模型的感知域依旧比原始分辨率小的多。 ours: 在FPN的基础上提出里两个模块 1 .CEM:上下文提取模型,从多个接受域中提取大量上下文信息,但是导致了冗余的上下文信息。 CxAM:专注于识别语义信息 CnAM:专注于定位精确的位置。 网络结构:Bin-...
To address this problem, we propose a novel end-to-end Attention Feature Pyramid Transformer Network framework to learn the object detectors with multi-scale feature maps via a transformer encoder-decoder fashion. AFPN learns to aggregate pyramid feature maps with attention mechanisms. Specifically, ...
object detection; convolutional neural network; self-attention; feature pyramid network MSC: 68Q041. Introduction With the development of neural network, many detectors based on CNN and Transformer-based architectures have been proposed in recent years [1,2]. Among them, the feature pyramidal ...
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...
code and model of Pyramid Feature Selective Network for Saliency detection - CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection
git@gitee.com:liu-daxing/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection.git liu-daxing PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection 北京奥思研工智能科技有限公司版权所有...