Quantitative results for different metrics under varying levels of occlusion in terms of mIoU on the Youtube-VIS 2019 dataset.

<p>Quantitative results for different metrics under varying levels of occlusion in terms of mIoU on the Youtube-VIS 2019 dataset.</p>

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Bibliographic Details
Main Author:	Farnoosh Arefi (19812520) (author)
Other Authors:	Amir M. Mansourian (19812523) (author), Shohreh Kasaei (14573459) (author)
Published:	2024
Subjects:	Biochemistry Genetics Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified robust similarity measure ovis datasets highlight noise channel reduction lack sufficient information feature map extracted deep spectral improvement based channel reduction incorrect instance segments extracted instance segments received less attention image decomposition process deep spectral methods instance segmentation purposes instance segmentation due effective instance segmentation using boc instead low standard deviation graph partitioning task feature map values div >< p conventional dot product dcr prunes channels ncr retains channels instance segmentation dot product methods reframe less likely image localization dcr ). vis 2019 tasks within supervised learning qualitative results potentially leading paper demonstrates paper addresses obtain eigensegments metric considers lower entropy improvements achieved growing interest commonly used affinity matrix
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