Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. However, this task was seldom explored. In this work, based on IPMT, a state-of-the-art few-shot image segmentation method that combines external support guidance information with adaptive query guidance cues, we propose to leverage multi-grained temporal guidance information for handling the temporal correlation nature of video data. We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively. Frame prototypes are further used for each frame independently to handle fine-grained adaptive guidance and enable bidirectional clip-frame prototype communication. To reduce the influence of noisy memory, we propose to leverage the structural similarity relation among different predicted regions and the support for selecting reliable memory frames. Furthermore, a new segmentation loss is also proposed to enhance the category discriminability of the learned prototypes. Experimental results demonstrate that our proposed video IPMT model significantly outperforms previous models on two benchmark datasets. Code is available at https://github.com/nankepan/VIPMT.

翻译：小样本视频目标分割（FSVOS）旨在利用少量标注支持图像定义的相同类别，在查询视频中分割目标物体。然而，该任务此前鲜有研究。本文基于当前最先进的小样本图像分割方法IPMT（该方法融合了外部支持引导信息与自适应查询引导线索），提出利用多粒度时序引导信息处理视频数据的时间关联特性。我们将查询视频信息分解为片段原型和记忆原型，分别用于捕获局部与长期的内部时序引导信息。进一步地，对每帧独立使用帧原型实现细粒度自适应引导，并支持双向的片段-帧原型通信。为减少噪声记忆的影响，我们提出利用不同预测区域间的结构相似性关系及支持信息，以选择可靠的记忆帧。此外，还提出了一种新的分割损失函数，以增强所学原型的类别判别能力。实验结果表明，本文提出的视频IPMT模型在两个基准数据集上显著优于此前模型。代码见 https://github.com/nankepan/VIPMT。