Occluded Video Instance Segmentation with Set Prediction Approach

被引：0

作者：

Bae, Heechul ^{[1
]}

Song, Soonyong ^{[1
]}

Park, Junhee ^{[1
]}

机构：

[1] Elect & Telecommun Res Inst ETRI, Ind & IoT Intelligence Res Dept, Daejeon, South Korea

来源：

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) | 2021年

关键词：

D O I：

10.1109/ICCVW54120.2021.00429

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Occluded Video Instance Segmentation (OVIS) is a multi-task problem performing detection, segmentation, and tracking simultaneously under severe occlusions. We propose an extended model for the OVIS task based on the real-time one-stage instance segmentation method. The proposed model was applied to the OVIS dataset hold by the ICCV 2021 - Occluded Video Instance Segmentation Workshop 2021. We also show that the occlusions can be handled efficiently through one-stage approaches.

引用

页码：3843 / 3846

页数：4

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