Salient Object Detection 坏牧羊人 6 人赞同了该文章 概述 显著性目标检测也被称为显著性检测,旨在通过模拟人类视觉感知系统来检测自然场景图像中最显著的目标和区域。虽然,显著性目标检测听名字是一个检测任务,但是实际上是一个图像分割任务,即一个像素级分类任务,是一个数据所驱动的一个任务。是将自然图像中的显...
2、allows each spatial location to view the local context at different scale spaces, context at different scale spaces joint training:edge data(BCE)和 salient data (CE)交替训练,细化edge。 Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection (2019 CVPR) depth label 与 SO...
太长不看版本: 这篇文章主要针对video salient ob-ject detection (VSOD)问题提出了Pyramid Constrained Self-Attention模块,这个模块基于non-local,将关联性分析时的Q和整个K相乘,变为了Q和K中一定范围内的像素点相乘(因为视频的特性:相邻帧之间所关注物体的变动不会特别大,所以重点关注那一块区域就行了),同时加入...
它们也是《Salient Object Detection: A Survey》一文所主要关注的。这三篇(注意观察前两篇的版本)文章分别是: 1、T. Liu, J. Sun, N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,” in CVPR,2007, pp. 1–8. 这篇文章将显著性检测定义为图像分割问题,它将显著性目...
Salient Object Detection with Pyramid Attention and Salient Edges 主要亮点 网络结构 Pyramid Attention Module(2c) Salient Edge Detector(2e) Dense connection Discussion Detailed Network Architecture 实验细节 相关链接 主要亮点 金字塔注意力模块 显著性边缘检测模块 ...
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BASNet: Boundary Aware Salient Object Detection 网络结构 主要亮点 损失函数 实验细节 相关链接 网络结构 主要亮点 深监督编解码器(sup1~sup8,可见前图) 额外的残差细化模块 使用了一种混合了BCE、SSIM(结构相似性)、IOU三种损失的混合损失 to supervise the training process of accurate salient object prediction...
Salient object detectionVisual saliencyFeature learningFully convolutional neural networkIn recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods...
它实则为图像分割任务，将像素级分类，依据数据驱动原则，将自然图像中的显著目标细分出来。显著目标通常视为前景部分，下图示意了这一过程，展示为像素级别的二分类任务。早期方法基于手工特征，采用传统方法进行。随着深度学习兴起，卷积神经网络方法在显著性目标检测上表现突出，Transformer架构的应用亦提升了...
内容提示: Reverse Attention for Salient Object DetectionShuhan Chen, Xiuli Tan, Ben Wang, and Xuelong HuSchool of Information Engineering,Yangzhou University, China{c.shuhan, t.xiuli0214}@gmail.com, wangben9503@163.com, xlhu@yzu.edu.cnAbstract. Benef i t from the quick development of deep ...