Segmentation by Per-Region Prediction 由于直接使用区域作为预测目标,因此相对于每像素预测能够大大减少计算量。 作者在 Transformer 主干的最后一层输出上应用了线性分类器,线性层将会为所有 tokens 生成一个类别: 线性层 由于前面一节中已经为tokens生成了相对应的区域形状(region geometrical description),因此只需要简...
Existing Semantic Segmentation Models as Parametric Prototype Learning 作者首先介绍了现有的几种参数可学习的方法 Parametric Softmax Projection 几乎所有的卷积网络以及大部分Transformer结构的网络采用了这种设计,他们的模型主要包括两个可学习部分: 用于密集视觉特征提取的编码器 \phi 将像素特征投影到语义标签空间的分...
amusi/CVPR2025-Papers-with-Code Star19.9k Code Issues Pull requests CVPR 2025 论文和开源项目合集 pythonmachine-learningcomputer-visiondeep-learningpaperimage-processingtransformerstransformerobject-detectionimage-segmentationvisual-trackingsemantic-segmentationcvprcvpr2020cvpr2021cvpr2022cvpr2023cvpr2024cvpr2025 ...
现有的基于CNN semantic segmentation网络大都是对前面分类网络得到的label map(FCN中是16*16)做基于bilinear interpolation的deconvolution. 然而这种deconvolution 的输入是前面经过convolution 和pooling 的 feature map这个feature map已经失去了很多structured details, 往往使用deconvolution不能得到很好的效果。
Application of thermal imaging in the CM of industrial infrastructure has been the topic of several papers. Different traditional image processing (TIP) methods have been proposed for thermal image segmentation. The proposed methodologies can be categorized into four different groups, namely: region-base...
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade CVPR2017 本文针对语义分割问题,引入Cascade思想,将图像像素根据分割难易度分为三个等级:Easy、Moderate、Hard ,分别用CNN网络的浅层、中层、高层完成分割。降低运算量同时提高分割精度 ...
The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoi
3D Semantic Segmentation Weakly-Supervised Semantic Segmentation Show all 29 subtasks Most implemented papers Most implemented Social Latest No code U-Net: Convolutional Networks for Biomedical Image Segmentation labmlai/annotated_deep_learning_paper_implementations • • 18 May 2015 There is ...
Multimodal fusion can make semantic segmentation more robust. However, fusing an arbitrary number of modalities remains underexplored. To delve into this problem, we create the DeLiVER arbitrary-modal segmentation benchmark, covering Depth, LiDAR, multiple Views, Events, and RGB. Aside from this, we...