Therefore, a multi‐scale feature fusion pyramid attention network (PAN) for single image dehazing is proposed. In PAN, combined with the attention mechanism, a shallow and deep feature fusion (SDF) strategy is designed. SDF considers multi‐scale as well as channel‐level fusion to pro...
image dehazingmulti-scale netfeature fusionattentionIn order to remove the haze from hazy images, we design an end-to-end trainable multi-scale feature ... B Hu - 《Pattern Recognition & Image Analysis》 被引量: 0发表: 2021年 A Multistage with Multiattention Network for Single Image Dehazing...
作者认为FPN和SSD中的特征金字塔只利用了主干网络的不同stage的特征进行目标检测,而主干网络最初是针对分类问题设计的,觉得在特征金字塔这块还有提升的空间。所以提出M2Det模型,主要是Multi-Level Feature Pyramid Network(MLFPN)模块,其由Thinned U-shape Modules(TUM),Feature Fusion Modules(FFM)和Scale-wise Feature ...
In this article, a novel Multi-Scale Feature Progressive Fusion Network (MFPF-Net) is proposed for remote sensing image CD, which aims to fully fuse bi-temporal remote sensing images, exchange feature information, promote information propagation and achieve better detection results. In MFPF-Net, ...
SFAM聚合TUMs产生的多级多尺度特征,以构造一个多级特征金字塔。第一步,SFAM沿着channel维度将拥有相同scale的feature map进行拼接,这样得到的每个scale的特征都包含了多个level的信息。第二步,借鉴SENet的思想,加入channel-wise attention,以更好地捕捉有用的特征。SFAM的细节如下图所示: ...
We improved a series of backbones using CenterNet, output the fusion of multiple feature maps in backbone, and used the feature pyramid network (FPN) mechanism for multiscale object detection. Additionally, we improved the head of the detector and considered adding intersection over union (IoU) ...
PMJAF-Net: Pyramidal multi-scale joint attention and adaptive fusion network for explainable skin lesion segmentation Haiyan Li, ... Pengfei Yu, in Computers in Biology and Medicine, 2023 3.4 Efficient Pyramid Multi-Scale Channel Attention Modules Feature Decoder Modules: The U-shaped networks typica...
In addition, SFAM aggregates the features into the multi-level feature pyramid through a scale-wise feature concatenation operation and an adaptive attention mechanism. More details about the three core modules and network configurations in M2Det are introduced in the following.M2Det的整体架构如图...
Specifically, we present a consecutive multiscale feature-learning network (CMSFL-Net) that employs a consecutive feature-learning approach based on the usage of various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. In the conducted experiments ...
multi-level multiscale features. In addition, SFAM aggregates the features into the multi-level feature pyramid through a scale-wise feature concatenation operation and an adaptive attention mechanism. More details about the three core modules and network configurations in M2Det are introduced in ...