Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification. However, how to effectively organize information in the continuously growing feature bank remains under-explored, and this leads to inefficient design of the bank. We introduce an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features. We also design a new confidence loss and a fine-grained segmentation module to enhance the segmentation accuracy in uncertain regions. On public benchmarks, our algorithm outperforms existing state-of-the-arts.
PDF Abstract NeurIPS 2020 PDF NeurIPS 2020 AbstractCode
Datasets
Introduced in the Paper:
Long Video Dataset
Long Video Dataset (3X)
Used in the Paper:
DAVIS
DAVIS 2017
YouTube-VOS 2018
Referring Expressions for DAVIS 2016 & 2017
Results from the Paper
Ranked #2 on
Semi-Supervised Video Object Segmentation
on Long Video Dataset (3X)
(using extra training data)
