Superpixel Segmentation with Fully Convolutional Networks
时间: 2023-10-01 20:09:17 浏览: 42
《Superpixel Segmentation with Fully Convolutional Networks》是一篇发表于2016年的论文,由来自加拿大滑铁卢大学的 Jonathan Long、Evan Shelhamer 和 Trevor Darrell 等人撰写。该论文提出了一种基于全卷积网络 (FCN) 的超像素分割方法,称为 SS-FCN (Superpixel Segmentation Fully Convolutional Network)。
该方法将超像素分割和全卷积网络相结合,利用超像素池化和全卷积网络的优势,实现了对图像的超像素分割和分析。具体来说,该方法首先使用基于 SLIC 算法的超像素分割方法,将输入图像分割成多个超像素块;然后利用全卷积网络提取图像特征,并将特征图像映射到超像素块的范围内,从而得到一个固定大小的特征向量。最后,基于超像素掩膜和特征向量,使用一个 1x1 的卷积层输出每个像素属于超像素块的概率,从而实现对图像的超像素分割和分析。
该方法在多个数据集上进行了实验,取得了较好的分割效果,并且具有较高的计算效率。同时,该方法还可以与其他分割算法结合使用,进一步提高分割效果。该论文的提出,为基于全卷积网络的分割方法提供了新的思路和实现方式。
相关问题
fully convolutional networks for semantic segmentation
完全卷积网络(Fully Convolutional Networks, FCN)是用于语义分割的一种深度学习模型。它通过将全连接层替换为卷积层来保留输入图像的空间信息,并通过上采样或反卷积操作来恢复高分辨率的分割结果。这种模型在处理大尺寸图像时特别有效。
Transformer-Based Visual Segmentation: A Survey
Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.
This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.
The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.
Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.