Juliette Bertrand presents Self-supervised video object segmentation by motion grouping
On 2022-01-20 11:00:00 at https://feectu.zoom.us/j/98555944426
"Self-supervised video object segmentation by motion grouping", ICCV 2021
Paper url:
https://arxiv.org/pdf/2104.07658.pdf
Paper abstract:
Animals have evolved highly functional visual systems to understand motion,
assisting perception even under complex environments. In this paper, we work
towards developing a computer vision system able to segment objects by
exploiting motion cues, i.e. motion segmentation. We make the following
contributions: First, we introduce a simple variant of the Transformer to
segment optical flow frames into primary objects and the background. Second, we
train the architecture in a self-supervised manner, i.e. without using any
manual annotations. Third, we analyze several critical components of our method
and conduct thorough ablation studies to validate their necessity. Fourth, we
evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2,
and FBMS59). Despite using only optical flow as input, our approach achieves
superior or comparable results to previous state-of-the-art self-supervised
methods, while being an order of magnitude faster. We additionally evaluate on
a
challenging camouflage dataset (MoCA), significantly outperforming the other
self-supervised approaches, and comparing favourably to the top supervised
approach, highlighting the importance of motion cues, and the potential bias
towards visual appearance in existing video segmentation models.
See the page of reading groups
http://cmp.felk.cvut.cz/~toliageo/rg/index.html
Paper url:
https://arxiv.org/pdf/2104.07658.pdf
Paper abstract:
Animals have evolved highly functional visual systems to understand motion,
assisting perception even under complex environments. In this paper, we work
towards developing a computer vision system able to segment objects by
exploiting motion cues, i.e. motion segmentation. We make the following
contributions: First, we introduce a simple variant of the Transformer to
segment optical flow frames into primary objects and the background. Second, we
train the architecture in a self-supervised manner, i.e. without using any
manual annotations. Third, we analyze several critical components of our method
and conduct thorough ablation studies to validate their necessity. Fourth, we
evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2,
and FBMS59). Despite using only optical flow as input, our approach achieves
superior or comparable results to previous state-of-the-art self-supervised
methods, while being an order of magnitude faster. We additionally evaluate on
a
challenging camouflage dataset (MoCA), significantly outperforming the other
self-supervised approaches, and comparing favourably to the top supervised
approach, highlighting the importance of motion cues, and the potential bias
towards visual appearance in existing video segmentation models.
See the page of reading groups
http://cmp.felk.cvut.cz/~toliageo/rg/index.html