We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotate...
One-Shot Video Object Segmentation论文笔记 要解决的问题 半监督的视频目标分割 方法 给出一帧或多帧人工标注的分割掩码,学习追踪特定目标的模型,然后将后续的视频序列的所有像素分割为前景与背景 原理 基于一个Fully-CNN,可以连续的传递generic semantic information(在ImageNet上学习,用于前景分割任务),最后学习test ...
论文标题:One-Shot Video Object Segmentation 源代码:www.vision.ee.ethz.ch/˜cvlsegmentation/osvos/ motivation 计算机视觉领域中,Image和目标有关的经典任务有三种:分类、检测和分割。其中分类是实现“是什么”,检测和分割都可以实现定位,前者是依照边界框形式,而后者是像素级的分割。 Video实现目标检测任务有...
One-Shot Video Object Segmentation,基于单帧标记的视频物体分割,对于一个视频中的某一个物体,我们只提供一张训练样本,怎么把视频里所有的该物体分割出来? 上图第一张图像是标记样本,其他的图像是分割的结果。 这里我们使用CNN网络来完成上述任务 One-Shot Video Object Segmentation (OSVOS) 算法总体的思路如下: 先...
作者提出One-Shot Video Object Segmentation (OSVOS)算法,具体流程分为三步:1.采用FCN架构,在ImageNet数据集上进行预训练,学习到通用的语义信息。2.在DAVIS 2016的训练集上进行fine-tune,学习前景分割。3.最后在测试序列的第一帧上fine-tune,学习到在后面帧中要进行分割的某特定目标的外形等信息,然后对后续帧...
项目地址:http://www.vision.ee.ethz.ch/~cvlsegmentation/osvos/ 摘要 本论文解决了半监督视频物体分割的问题,即只给第一帧的掩模(mask)从视频背景中分离出物体。我们提出OSVOS的方法,该方法基于神经网络结构,可以成功将ImageNet数据集学习到的特征迁移到视频分割任务中,并且以极大的优势取得了state-of-the-art水...
One-Shot Video Object Segmentation (OSVOS) is a CNN architecture to tackle the problem of semi-supervised video object segmentation, which performs the separation of an object from the background in a frame-independent way with the aid of one manually-segmented frame. However, in the scenarios ...
OSVOS: One-Shot Video Object Segmentation Check our project page for additional information. OSVOS is a method that tackles the task of semi-supervised video object segmentation. It is based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic ...
This is the implementation of our workOne-Shot Video Object Segmentation (OSVOS), for semi-supervised video object segmentation. OSVOS is based on a fully convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of...
Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot principle, fine-tuning VOS methods train a segmentation model separately...