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
this https URL

通过引入多粒度的时间引导信息和自适应查询引导线索，利用剪辑和内存原型来分别捕捉本地和长期内部时间引导，从而在处理视频数据的时间相关性时，通过每个帧独立地使用帧原型来处理细粒度的自适应引导并实现双向的剪辑 - 帧原型通信，以减少噪声内存的影响，并提出了一种新的分割损失来增强原型的类别可辨识性。实验结果表明，我们提出的视频 IPMT 模型在两个基准数据集上明显优于先前的模型。