论文标题
CRVO：视频对象细分的线索提炼网络
CRVOS: Clue Refining Network for Video Object Segmentation
论文作者
论文摘要
基于编码器的半监督视频对象分割（半VOS）的方法由于其出色的性能而受到了广泛的关注。但是，它们中的大多数具有复杂的中间网络，这些网络会产生强大的指定符在具有挑战性的情况下，这是可靠的，并且在处理相对简单的方案时，这是相当低效的。为了解决此问题，我们提出了一个实时网络，线索提炼网络用于视频对象细分（CRVO），该网络没有任何中间网络可以有效处理这些方案。在这项工作中，我们提出了一个简单的说明符，称为线索，其中由上一个框架的粗蒙版和协调信息组成。我们还提出了一个新颖的精炼模块，该模块通过使用反卷积层而不是双线性上采样层，显示出与一般性能相比较好的性能。我们提出的方法以竞争精度显示了现有方法中最快的速度。在戴维斯（Davis）2016验证集中，我们的方法达到63.5 fps，J＆F得分为81.6％。
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of them have complex intermediate networks which generate strong specifiers to be robust against challenging scenarios, and this is quite inefficient when dealing with relatively simple scenarios. To solve this problem, we propose a real-time network, Clue Refining Network for Video Object Segmentation (CRVOS), that does not have any intermediate network to efficiently deal with these scenarios. In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame's coarse mask and coordinates information. We also propose a novel refine module which shows the better performance compared with the general ones by using a deconvolution layer instead of a bilinear upsampling layer. Our proposed method shows the fastest speed among the existing methods with a competitive accuracy. On DAVIS 2016 validation set, our method achieves 63.5 fps and J&F score of 81.6%.
