  Although deep learning based methods have achieved great progress in
unsupervised video object segmentation, difficult scenarios (e.g., visual
similarity, occlusions, and appearance changing) are still not well-handled. To
alleviate these issues, we propose a novel Focus on Foreground Network (F2Net),
which delves into the intra-inter frame details for the foreground objects and
thus effectively improve the segmentation performance. Specifically, our
proposed network consists of three main parts: Siamese Encoder Module, Center
Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module.
Firstly, we take a siamese encoder to extract the feature representations of
paired frames (reference frame and current frame). Then, a Center Guiding
Appearance Diffusion Module is designed to capture the inter-frame feature
(dense correspondences between reference frame and current frame), intra-frame
feature (dense correspondences in current frame), and original semantic feature
of current frame. Specifically, we establish a Center Prediction Branch to
predict the center location of the foreground object in current frame and
leverage the center point information as spatial guidance prior to enhance the
inter-frame and intra-frame feature extraction, and thus the feature
representation considerably focus on the foreground objects. Finally, we
propose a Dynamic Information Fusion Module to automatically select relatively
important features through three aforementioned different level features.
Extensive experiments on DAVIS2016, Youtube-object, and FBMS datasets show that
our proposed F2Net achieves the state-of-the-art performance with significant
improvement.
