我们提出了CONTAINER(CONText AggregatIon NEtwoRk),这是一个多头上下文聚合的通用构建模块,能够像Transformers一样利用长程交互,同时仍然利用局部卷积操作的归纳偏差,从而实现更快的收敛速度,这通常在CNNs中可见。我们的CONTAINER架构在ImageNet上使用22M参数实现了82.7%的Top-1准确率,相比DeiT-Small提高了2.8个百分点,并且...
核心代码 classContextAggregation(nn.Module):def__init__(self, in_channels, reduction=1):super(ContextAggregation,self).__init__()self.in_channels = in_channelsself.reduction = reductionself.inter_channels =max(in_channels // reduction,1) conv_params =dict(kernel_size=1, act_cfg=None)self...
(2) Boundary guided Context Aggregation (BCA) module:利用上述获得的边缘图引导上下文信息聚合,获得鲁棒性更强的特征图; 总结来说,MSB模块进行边缘检测提取边缘图,则BCA模块利用边缘图进行上下文信息完成语义分割任务,边缘检测和语义分割两个子任务共享同一个backbone。 3.2 Multi-Scale Boundary (MSB) extractor MSB...
https://github.com/mahaoxiang822/Boundary-Guided-Context-Aggregation/blob/main/model/BCANet.py importtorchimporttorch.nn.functionalasFfromtorchimportnnfrommodel.dilated_resnetimportget_resnet50_baseline,get_resnet101_baselineclassBCA(nn.Module):def__init__(self,xin_channels,yin_channels,mid_channels...
Our ACE module can be embedded into other Convolutional Neural Networks (CNNs) easily for context aggregation. The effectiveness of the proposed module is demonstrated on Pascal-Context and ADE20K datasets. Although our proposed ACE only consists of three deformable convolution blocks, it outperforms...
先构造一个Context Prior Layer,包含一个由Affinity Loss监督生成的Context Prior Map和一个采用完全可分离的卷积的Aggregation Module,用来获取空间信息以推理关系。 图2 Context Prior Layer Context Prior Layer包含一个聚合模块和一个上下文由亲和损失监督的先验映射。将主干网络提取的输入特征通过聚合模块聚合空间信息。
(1)上下文先验层(Context Prior Layer)包括:聚合模块(Aggregation Module)、上下文先验映射( Context Prior Map)由亲和损失(Affinity Loss)监督。 (2)概述: 利用骨干网络(backbone)得到的输出作为聚合模块的输入聚合空间信息用来推理上下文关系。 生成一个点方向的上下文先验映射,由亲和损失监督(亲和损失:构造一个理想亲...
Methods: In this study, we (1) build a reliable deep learning network framework, named DCACNet, to improve the segmentation performance for medical images; (2) propose a multiscale cross-fusion encoding network to extract features; (3) build a dual context aggregation module to fuse the ...
• Context Aggregation Module: The Context Aggregation Module has the ability to combine the context information from the Context Representation Module. This mod- ule produces combined contexts. The combined context can be used to provide better information with users because they can understand ...
\mathcal{F}_{agg}(E_i,E;\Theta_a)是第i个特征的上下文信息聚合函数,即aggregation model,使用了Embedding Layer的输出E和feature embeddingE_i作为输入; \mathcal{F}_{project}(\mathcal{F}_{agg};\Theta_p)则则是一个映射函数,即projection model,将聚合后的上下文信息\mathcal{F}_{agg}投影到featu...