摘要\quad 我们提出了 LSeg,一种用于语言驱动的语义图像分割的新模型。 LSeg 使用文本编码器计算给定的输入标签(例如,“草”或“建筑物”)的编码和使用图像编码器计算输入图像的每个像素的编码。图像编码器使…
LANGUAGE-DRIVEN SEMANTIC SEGMENTATION论文阅读笔记 摘要 文章的主要贡献是提出了一种新的语言驱动的分割模型LSeg,其使用Text encoder编码描述性的输入标签,使用Image encoder计算图像的逐像素的embedding。图像编码器使用的是对比目标训练,目的是将像素的embedding与对应文本标签的embedding进行对齐。text embedding提供了灵活的...
Language-driven Semantic Segmentationopenreview.net/forum?id=RriDjddCLN 摘要 提出了一种新的语言驱动的语义图像分割模型LSeg。LSeg使用文本编码器与基于transformer的图像编码器一起计算描述性输入标签(例如,“草”或“建筑物”)的嵌入,该图像编码器计算输入图像的密集像素嵌入。图像编码器用一种对比目标训练,目的...
通过矩阵相乘将文本和图像结合起来了。训练时可以学到language aware(语言文本意识)的视觉特征。从而在最后推理的时候能使用文本的prompt任意的得到分割的效果。 本文中文本编码器的参数完全使用的CLIP的文本编码器的参数,因为分割任务的数据集都比较小(10-20万),为保证文本编码器的泛化性,就直接使用并锁住CLIP中文本编...
We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., ''grass'' or 'building'') together with a transformer-based image encoder that computes dense per-pixel embeddings of the input im...
实验主要进行了三部分:Language-Driven Semantic Segmentation、Unsupervised Semantic Segmentation以及Instance Mask Tracking。 以Language-Driven Semantic Segmentation为例: 需要注意的是这里对比的GroupViT等方法的训练策略与文章方法有所不同,作者直接选取了这些方法最好的结果进行对比。同时,作者将Pascal Context的数据按照...
This large number of class categories also induces a large natural class imbalance, both of which are challenging for existing 3D semantic segmentation methods. To learn more robust 3D features in this context, we propose a language-driven pre-training method to encourage learned 3D features that ...
both of which are challenging for existing 3D semantic segmentation methods. To learn more robust 3D features in this context, we propose a language-driven pre-training method to encourage learned 3D features that might have limited training examples to lie close to their pre-trained text embedding...
L. Self-supervised driven consistency training for annotation efficient histopathology image analysis. Med. Image Anal. 75, 102256 (2022). Article PubMed Google Scholar Tizhoosh, H. R. & Pantanowitz, L. Artificial intelligence and digital pathology: challenges and opportunities. J. Pathol. ...
(i) the left ATL houses lexical representations that support semantically driven speech production50,51; or (ii) that the bilateral ATL-hub semantic system connects to left-lateralised prefrontal speech production systems from the left ATL17,20. Although both theories explain the differential anomia...