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...
原文是:《SFNet:Learning Object-aware Semantic Correspondence》 code是:https://cvlab-yonsei. github.io/projects/SFNet 本文是cvpr2019的文章,本文提出的网络结构名为SFNet,其内容描述的如标题所示,主要是学习对象(object)的一个语义(Semantic)对应性(correspondence)。在现实生活中,人的语言就是一个世界的简化与...
In the shared gradient attention module, the edge consistency between affordance detection results and semantic edge detection results is enhanced by sharing gradient attention weights. Our experiments show that our framework can output affordance detection results with better edge quality. In particular, ...
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…
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 ...
we propose a contextual semantic consistency network (CSCNet) to cross these gaps. First, we use a context-aware transfer sub-network to obtain the intermediate representations describing social and physical interactions. Second, we transfer these data with distribution shifts and different manifestations...
The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level
Semi-supervised semantic seg- mentation with directional context-aware consistency. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 1205–1214, 2021. 1, 2, 3, 5, 6, 8 [36] Junnan Li, Caiming Xiong, and Steven CH...
& Jia, Y. MCAN: multimodal cross-aware network for fake news detection by extracting semantic-physical feature consistency. J Supercomput 81, 299 (2025). https://doi.org/10.1007/s11227-024-06815-1 Download citation Accepted06 December 2024 Published16 December 2024 DOIhttps://doi.org/10.1007/...
aiming to capture the semantic consistency between audio and visual data. Meanwhile, we introduce a hyperbolic space to model the hierarchical structure of the data itself. Moreover, the proposed approach includes a novel loss function that considers the relationships between input modalities, reducing...