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Title:Semantic amodal video segmentation using a synthetic dataset
Author(s):Hui, Kexin
Advisor(s):Schwing, Alexander
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Abstract:In this work, we provide tools for annotating both object category and shot transitions for a new semantic modal instance-level object segmentation dataset. This new dataset provides ample opportunities to train models for instance-level segmentation, both modal and amodal. Moreover, in this work, we also present results for instance-level segmentation using ResNet-based DeepLab, a state-of-the-art semantic image segmentation model. We also develop a new semantic amodal instance-level video segmentation model based on DeepLab for the aforementioned dataset. Our model for amodal segmentation operates on a per-frame basis, and the model is guided by the modal mask estimated from the current frame and from previous frames delineating the object of interest. We demonstrate the efficacy of the proposed model on the new dataset.
Issue Date:2018-12-14
Rights Information:Copyright 2018 Kexin Hui
Date Available in IDEALS:2019-02-08
Date Deposited:2018-12

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