CRVOS：用于视频对象分割的线索细化网络
CRVOS: Clue Refining Network for Video Object Segmentation
摘要：
基于编解码器的半监督视频对象分割（Semi-VOS）方法因其优越的性能而受到广泛关注。然而，它们中的大多数都具有复杂的中间网络，这些中间网络生成强大的说明符，以应对具有挑战性的场景，这在处理相对简单的场景时是非常低效的。为了解决这个问题，我们提出了一个实时网络，视频对象分割线索细化网络（CRVOS），它没有任何中间网络来有效地处理这些场景。在这项工作中，我们提出了一个简单的说明符，称为线索，它包括前一帧的粗掩码和坐标信息。我们还提出了一种新的细化模块，通过使用反卷积层代替双线性上采样层，与一般的细化模块相比，该模块显示出更好的性能。我们提出的方法显示了最快的速度与竞争力的准确性之间的现有方法。在DAVIS 2016验证集上，我们的方法达到了63.5 fps，J&F得分为81.6%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
