Unsupervised Video Object Segmentation (UVOS) refers to the challenging task of segmenting the prominent object in videos without manual guidance. In recent works, two approaches for UVOS have been discussed that can be divided into: appearance and appearance-motion-based methods, which have limitations respectively. Appearance-based methods do not consider the motion of the target object due to exploiting the correlation information between randomly paired frames. Appearance-motion-based methods have the limitation that the dependency on optical flow is dominant due to fusing the appearance with motion. In this paper, we propose a novel framework for UVOS that can address the aforementioned limitations of the two approaches in terms of both time and scale. Temporal Alignment Fusion aligns the saliency information of adjacent frames with the target frame to leverage the information of adjacent frames. Scale Alignment Decoder predicts the target object mask by aggregating multi-scale feature maps via continuous mapping with implicit neural representation. We present experimental results on public benchmark datasets, DAVIS 2016 and FBMS, which demonstrate the effectiveness of our method. Furthermore, we outperform the state-of-the-art methods on DAVIS 2016.

翻译：无监督视频对象分割（UVOS）是指在无人工引导条件下分割视频中显著目标的挑战性任务。近年来的研究提出了两种UVOS方法，可归纳为：基于外观的方法与基于外观-运动的方法，二者各有局限。基于外观的方法因利用随机配对帧间的关联信息，未考虑目标对象的运动特征；而基于外观-运动的方法则因将外观与运动融合，导致对光流依赖性过强。本文提出了一种新颖的UVOS框架，能够从时间与尺度两个维度解决上述两类方法的局限性。其中，时间对齐融合模块通过将相邻帧的显著性信息与目标帧对齐，充分利用相邻帧信息；尺度对齐解码器则借助隐式神经表示的连续映射，聚合多尺度特征图以预测目标对象掩码。我们在公开基准数据集DAVIS 2016与FBMS上的实验结果表明了该方法的有效性，并在DAVIS 2016上超越了现有最优方法。