MOVE: Motion-Guided Few-Shot Video Object Segmentation
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.
6889763131e1218a08928191
Kaining Ying
6889763131e1218a08928192
Hengrui Hu
6889763131e1218a08928193
Henghui Ding
MOVE: Motion-Guided Few-Shot Video Object Segmentation
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.
A new dataset and baseline method for motion-guided few-shot video object segmentation are introduced, addressing challenges in motion understanding.
few-shot video object segmentation FSVOS motion-guided video object segmentation temporal dynamics Decoupled Motion Appearance Network DMA