Multi-grained Temporal Prototype Learning for Few-shot Video Object
  Segmentation
Nian Liu Kepan Nan Wangbo Zhao Yuanwei Liu Xiwen Yao Salman Khan Hisham Cholakkal Rao Muhammad Anwer Junwei Han Fahad Shahbaz Khan
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.