MOVE: Motion-Guided Few-Shot Video Object Segmentation
Authors: name: Kaining Ying, name: Hengrui Hu, name: Henghui Ding

This work addresses motion-guided few-shot video object segmentation (FSVOS),
which aims to segment dynamic objects in videos based on a few annotated
examples with the same motion patterns. Existing FSVOS datasets and methods
typically focus on object categories, which are static attributes that ignore
the rich temporal dynamics in videos, limiting their application in scenarios
requiring motion understanding. To fill this gap, we introduce MOVE, a
large-scale dataset specifically designed for motion-guided FSVOS. Based on
MOVE, we comprehensively evaluate 6 state-of-the-art methods from 3 different
related tasks across 2 experimental settings. Our results reveal that current
methods struggle to address motion-guided FSVOS, prompting us to analyze the
associated challenges and propose a baseline method, Decoupled Motion
Appearance Network (DMA). Experiments demonstrate that our approach achieves
superior performance in few shot motion understanding, establishing a solid
foundation for future research in this direction.