Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visually similar surroundings (appearance-based) or suffer from deterioration in the quality of their predictions because of dynamic background and inaccurate flow (flow-based). To overcome the limitations, we propose an implicit motion-compensated network (IMCNet) combining complementary cues ( i.e. , appearance and motion) with aligned motion information from the adjacent frames to the current frame at the feature level without estimating optical flows. The proposed IMCNet consists of an affinity computing module (ACM), an attention propagation module (APM), and a motion compensation module (MCM). The light-weight ACM extracts commonality between neighboring input frames based on appearance features. The APM then transmits global correlation in a top-down manner. Through coarse-to-fine iterative inspiring, the APM will refine object regions from multiple resolutions so as to efficiently avoid losing details. Finally, the MCM aligns motion information from temporally adjacent frames to the current frame which achieves implicit motion compensation at the feature level. We perform extensive experiments on $\textit {DAVIS}_{\textit {16}}$ and $\textit {YouTube-Objects}$ . Our network achieves favorable performance while running at a faster speed compared to the state-of-the-art methods. Our code is available at https://github.com/xilin1991/IMCNet .

中文翻译：

---

用于无监督视频对象分割的隐式运动补偿网络

无监督视频对象分割 (UVOS) 旨在自动将主要前景对象与视频序列中的背景分离。现有的 UVOS 方法要么在视觉上相似的环境（基于外观）时缺乏鲁棒性，要么由于动态背景和不准确的流动（基于流动）而导致预测质量下降。为了克服这些限制，我们提出了一个结合互补线索的隐式运动补偿网络（IMCNet）（ 即，外观和运动）在特征级别从相邻帧到当前帧的对齐运动信息，而不估计光流。所提出的 IMCNet 由亲和力计算模块（ACM）、注意力传播模块（APM）和运动补偿模块（MCM）组成。轻量级 ACM 基于外观特征提取相邻输入帧之间的共性。然后 APM 以自上而下的方式传输全局相关性。通过从粗到细的迭代启发，APM 将从多个分辨率中细化对象区域，从而有效地避免丢失细节。最后，MCM 将来自时间相邻帧的运动信息与当前帧对齐，从而在特征级别实现隐式运动补偿。我们进行了广泛的实验 $\textit {戴维斯}_{\textit {16}}$和 $\textit {YouTube 对象}$ . 与最先进的方法相比，我们的网络在以更快的速度运行的同时实现了良好的性能。我们的代码可在https://github.com/xilin1991/IMCNet .