952 research outputs found
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods.Comment: 10 pages, 6 figure
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
Deep learning-based video salient object detection has recently achieved
great success with its performance significantly outperforming any other
unsupervised methods. However, existing data-driven approaches heavily rely on
a large quantity of pixel-wise annotated video frames to deliver such promising
results. In this paper, we address the semi-supervised video salient object
detection task using pseudo-labels. Specifically, we present an effective video
saliency detector that consists of a spatial refinement network and a
spatiotemporal module. Based on the same refinement network and motion
information in terms of optical flow, we further propose a novel method for
generating pixel-level pseudo-labels from sparsely annotated frames. By
utilizing the generated pseudo-labels together with a part of manual
annotations, our video saliency detector learns spatial and temporal cues for
both contrast inference and coherence enhancement, thus producing accurate
saliency maps. Experimental results demonstrate that our proposed
semi-supervised method even greatly outperforms all the state-of-the-art fully
supervised methods across three public benchmarks of VOS, DAVIS, and FBMS.Comment: ICCV2019, code is available at
https://github.com/Kinpzz/RCRNet-Pytorc
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