Efficient One-Shot Video Object Segmentation

Video object segmentation is the problem of labelling the foreground object of interest that has widespread applications. We reevaluate One-shot Video Object Segmentation (OSVOS), a simple method that adapts VGG to image segmentation using a structure similar to a Fully Convolutional Network. We pro...

Full description

Saved in:
Bibliographic Details
Published in2020 7th NAFOSTED Conference on Information and Computer Science (NICS) pp. 320 - 325
Main Authors Hoang-Xuan, Nhat, Nguyen, E-Ro, Pham-Le, Thuy-Dung, Hoang-Nguyen, Khoi
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.11.2020
Subjects
boundary snapping module removal
Computer architecture
Computer science
convolutional neural nets
dice loss
focal loss
image classification
image segmentation
imbalanced binary classification
Labeling
neural net architecture
Object segmentation
one-shot video object segmentation
OSVOS competitive
U-net architecture
VGG
video signal processing
Online AccessGet full text

Cover

More Information
Summary:Video object segmentation is the problem of labelling the foreground object of interest that has widespread applications. We reevaluate One-shot Video Object Segmentation (OSVOS), a simple method that adapts VGG to image segmentation using a structure similar to a Fully Convolutional Network. We propose a range of improvements to make OSVOS competitive to newer methods while keeping its simplicity. Specifically, we replace VGG with EfficientNet, and adopt the U-net architecture. We also utilize Focal Loss and Dice Loss to handle the imbalanced binary classification, and finally we remove the boundary snapping module. With our amendments, we achieve 82.4% J&F on DAVIS 2016 validation set, an improvement over the original 80.2% of OSVOS. We also achieve much faster inference time per frame than OSVOS.
DOI:10.1109/NICS51282.2020.9335847