HIERARCHICAL EMBEDDING GUIDED NETWORK FOR VIDEO OBJECT SEGMENTATION

Chin Hsuan Shih, Wen Jiin Tsai

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Semi-supervised video object segmentation is to segment the target objects given the ground truth annotation of the first frame. Previous successful methods mostly rely on online learning or static image pre-train to improve accuracy. However, online learning methods require huge time costs at inference time, thus restrict their practical use. Methods with static image pre-train require heavy data augmentation that is complicated and time-consuming. This paper presents a fast Hierarchical Embedding Guided Network (HEGNet) which is only trained on Video Object Segmentation (VOS) datasets and does not utilize online learning. Our HEGNet integrates propagation-based and matching-based methods. It propagates the predicted mask of the previous frame as a soft cue and extracts hierarchical embedding at both deep and shallow layers to do feature matching. The produced label map of the deep layer is also used to guide the matching of the shallow layer. We evaluated our method on the DAVIS-2016 and DAVIS-2017 validation sets and achieved overall scores of 84.9% and 71.9% respectively. Our method surpasses the methods without online learning and static image pre-train and runs at 0.08 seconds per frame.

原文English
主出版物標題2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
發行者IEEE Computer Society
頁面1124-1128
頁數5
ISBN(電子)9781665441155
DOIs
出版狀態Published - 2021
事件2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
持續時間: 19 9月 202122 9月 2021

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
2021-September
ISSN(列印)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
國家/地區United States
城市Anchorage
期間19/09/2122/09/21

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