Semantic amodal video segmentation using a synthetic dataset
Hui, Kexin
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https://hdl.handle.net/2142/102966
Description
Title
Semantic amodal video segmentation using a synthetic dataset
Author(s)
Hui, Kexin
Issue Date
2018-12-14
Director of Research (if dissertation) or Advisor (if thesis)
Schwing, Alexander
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
ANNOTATION
VIDEO SEGMENTATION
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.
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