下采样:增加感受野,但是更加耗时且复杂,定位信息无法很好的得到,效果较差。 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。
Attention-guided pyramid context networks for detecting infrared small target under complex background [J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(4): 4250–4261. Article Google Scholar XIE S N, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for ...
Feature pyramid networkAttention mechanismDensity map generationCrowd counting has become a hot topic because of its wide applications in video surveillance and public security. However, one main problem of the deep learning methods for crowd counting ......
Attention-guided Context Feature Pyramid Network for Object Detection This repository re-implements AC-FPN on the base of Detectron-Cascade-RCNN. Please follow Detectron on how to install and use this repo. This repo has released CEM module without AM module, but we can get higher performance ...
本文总结近两年语义分割领域对 attention 和“低秩”重建机制的探索,并介绍笔者被 ICCV 2019 接收为 Oral 的工作:Expectation-Maximization Attention Networks for Semantic Segmentation(代码已开源:github.com/XiaLiPKU/EMANet)。注:本文中的 attention 仅指 self-attention,不涉及 soft-attention。
^abAdaptive Pyramid Context Network for Semantic Segmentation http://openaccess.thecvf.com/content_CVPR_2019/papers/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.pdf ^Expectation-Maximization Attention Networks for Semantic Segmentation https://arxiv.org/abs/1907.13426 ^An...
整合多内容信息注意力机制(multi-context attention mechanism)到CNN网络,得到人体姿态估计端到端的框架. 设计堆积沙漏网络(stacked hourglass networks) 生成不同分辨率特征的注意力图(attention maps),不同分辨率特征对应着不同语义层次信息. 利用CRF(Conditional Random Field)对注意力图中相邻区域的关联性进行建模. ...
Achieving a balance between preserving spatial details and maintaining high-level context remains elusive. Many denoising networks rely on single-scale local convolutions, leading to a limited receptive field and potentially inconsistent semantic outputs. Edge preservation is a concern, with many techniques...
Another example is of Feature Pyramid Networks [18], where the different layers’ features are combined via skip connections. However, feature fusion is usually implemented via simple summation and concatenation operations, which linearly aggregate the feature maps without having any knowledge about the...
These two modules in our LD-CGAN make the best use of text information to simplify the network; (2) To enrich feature representations in the highly structured model, we develop Pyramid Attention Refine Block (PAR-B) to capture multi-scale context. At each pyramid level, PAR-B takes coarse...