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We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time.
Video segmentation for the human head and shoulders is essential in creating elegant media for videoconferencing and virtual reality applications.
For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame.
This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively.
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance.
Ranked #1 on Semi-Supervised Video Object Segmentation on DAVIS 2016 (using extra training data)
Specifically, we first compute a pixel-wise similarity matrix by using representations of reference and target pixels and then select top-rank reference pixels for target pixel classification.
Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence.
Ranked #1 on Video Semantic Segmentation on Cityscapes val
In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77. 8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance.
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.