8.代码 GitHub - proteus1991/GridDehazeNet: This repo contains the official training and testing codes for our paper: GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing.
特征金字塔:利用不同尺度的特征去预测不同尺度的物体 SSD [29] and MSCNN [2] predict objects at multiple layers of the network without merging features. Feature pyramid networks [26] extend the backbone model with a top-down pathway that gradually recovers feature resolution from 1/32 to 1/4, ...
第二个问题在于早期层缺乏corase scale所提取到的特征,引入了多尺度的网络结构解决,也就是multi-scale,在每一层我们都产生所有scale的特征,有利于分类也有利于提取低层次特征,只有在经过多层处理后才有用。 关于这两个问题后面的部分会进一步详细说明,这也是文章的核心 Network 图为MSDNet整体结构,具体细节在下文中...
Multi-scale Interactive Network for Salient Object Detection CVPR20 摘要 本文提出MINet。在编码器中使用聚合交互模块AIM(aggregate interaction modules)来聚合相邻level的特征,由于仅使用小的up/down采样率,引入了很少噪声。在解码器中使用自交互模型SIM(self-interaction module)来利用multi-scale特征。 由于尺度变化造...
提示:文章写完后,目录可以自动生成,如何生成可参考右边的帮助文档 深度估计论文阅读_Multi-Scale Deep Network 前言 一、创新点 二、具体分析 1.粗细结合的网络架构 2.尺度不变误差 三、编程 前言 本篇博客主要对Depth Map Prediction from a Single Image using a Multi-Scale Deep Network进行理解和复现。... ...
In this paper, we propose a novel multi-scale attention network (MSA-Net) for image inpainting to fill the irregular missing regions. For extracting the multi-scale context gradually, we design a multi-scale attention group (MSAG), which consists of several multi-scale attention units (MSAUs)...
提出了一种用于快速多尺度目标检测的统一深度神经网络,即多尺度CNN (MS-CNN)。MS-CNN由建议子网络和检测子网络组成。在建议子网中,在多个输出层进行检测,使感受野匹配不同尺度的对象。这些互补的尺度特异性探测器被结合起来产生一个强大的多尺度目标探测器。通过优化多任务损失,实现了统一网络的端到端学习。此外,还...
Single image super-resolution via deep progressive multi-scale fusion networks Article 13 April 2022 A CNN-transformer hybrid network with selective fusion and dual attention for image super-resolution Article 23 February 2025 References Agustsson, E., Timofte, R.: NTIRE 2017 Challenge on sing...
MSA-Net Multi-Scale Attention Network for Crowd Counting 2019 作者:亚马逊 论文:https://arxiv.org/abs/1901.06026 创新点: 在backbone中就产生了多尺度的density map,经过上采样后,加入软注意力机制进行加权叠加。 提出了一个scale-aware loss,但是实验结果好像表明效果不大。... ...
scale feature maps output by LFFM are directly concatenated, and then the global average pooling operation is performed on the aggregated feature map to obtain the weights and multiply them with the aggregated feature map. The subsequent operations are the same as those of the MFPF-Net network....