论文标题
被运动背叛：通过运动分割伪装对象发现
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
论文作者
论文摘要
本文的目的是设计一种计算体系结构，该计算体系结构在视频中发现伪装的对象，特别是通过利用运动信息来执行对象分割。我们做出以下三个贡献：（i）我们提出了一种新型架构，该架构由两个基于伪装的重要组成部分组成，即，一个可区分的注册模块，以基于背景对齐连续帧，在背景下有效地强调差异图像中的对象边界，以及在动作对象的同时，在对象上置于运动序列，即使在某些方面都无法进行动作。 （ii）我们收集了第一个大规模移动的伪装动物（MOCA）视频数据集，该数据集由各种动物范围的140多个剪辑组成（67个类别）。 （iii）我们仅依靠运动来证明提出的模型对MOCA的有效性，并在Davis2016的无监督分段方案上实现竞争性能。
The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while maintaining the object permanence even when motion is absent at some point. (ii) We collect the first large-scale Moving Camouflaged Animals (MoCA) video dataset, which consists of over 140 clips across a diverse range of animals (67 categories). (iii) We demonstrate the effectiveness of the proposed model on MoCA, and achieve competitive performance on the unsupervised segmentation protocol on DAVIS2016 by only relying on motion.
