首先输入的是源图片与目标图片 I^s 与I^t ,随后进行特征的提取,提取的特征的组成使用的是一个孪生网络(siamese network),意思就是使用相同的网络提取特征。接着计算匹配得分,以及利用Kernel soft argmax计算 F^s , F^t ,最后结合mask M^s , M^t 计算三部分的loss, loss_{mask},loss_{flow},loss_{smo...
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency 收录于CVPR2021原文 Motivation 一致性在半监督语义分割中发挥了重大作用,一般意义上的一致性是通过约束弱数据增强方式(高斯模糊,色彩变化,翻转,旋转)的分割结果相同实现的,但是这种低层面的一致性约束是考虑了像素级别的一致性,而忽略了语义...
论文链接: https://jiaya.me/papers/semiseg_cvpr21.pdf代码链接: https://github.com/dvlab-research/Context-Aware-ConsistencyCVPR 2021的工作。 斜体是我自己的补充。Introduction 动机:在半监督设置中,模…
Additionally, considering devices with different communication and computing capabilities, investigating how a semantic communication network can be built and the performance gains that can be achieved for semantic communication in such heterogeneous systems are essential. Furthermore, the semantic-aware ...
Specifically, our models incorporate the global semantic from BERT and the local semantic from convolutional neural network together for monolingual detection and further explores the semantic consistency relationship for bilingual detection. The experimental results on the Chinese-English machine translation ...
Their approach, called the multi-level context refinement network (MCR-Net), includes two context refinement blocks: the inverted residual pyramid block (IRPB) and the context-aware fusion block (CFB). Show abstract Domain-invariant information aggregation for domain generalization semantic segmentation...
This paper presents CoopSLA (Cooperative Semantic Locality Awareness), a consistency model for cooperative editing applications running in resource-constrained mobile devices. In CoopSLA, updates to different parts of the document have different priorities, depending on the relative interest of the user ...
As a re- sult, we quantify the semantic awareness of image features as a form of attention and embed semantic consistency in enhancement network. Second, some methods [20, 55] propose to optimize im- age enhancement using color histogram in order to preserve t...
Context-Aware Consistency (the framework) Figure 1. Overview of the framework. 整个模型大致分为两个部分,Xl和Xu分别代表有标签数据和无标签数据。 有监督网络部分(Supervised Branch)同一般语义分割模型类似,由编码网络Encoder得到图像特征,之后利用分类器和标签对图像进行分类,得到分类器预测结果。 无监督网络部分...
Context-Aware Consistency 图4为框架流程图。如图所示,输入有两个分支(a),分别为 x_{l} (有标签 y_{l} )和 x_{u} (无标签样本)。 Supervised Branch: x_{l} 经过监督学习分支,即 x_{l} 经过Encoder network (E) ,输出特征图 f_{l} = E(x_{l})。分类器Classifier (C) 得到最终预测图, p...