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    MAIN: Multi-Attention Instance Network for Video Segmentation

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    Type
    Preprint
    Authors
    Alcazar, Juan Leon
    Bravo, Maria A.
    Thabet, Ali Kassem cc
    Jeanneret, Guillaume
    Brox, Thomas
    Arbelaez, Pablo
    Ghanem, Bernard cc
    KAUST Department
    Visual Computing Center (VCC)
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-04-11
    Permanent link to this record
    http://hdl.handle.net/10754/660665
    
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    Abstract
    Instance-level video segmentation requires a solid integration of spatial and temporal information. However, current methods rely mostly on domain-specific information (online learning) to produce accurate instance-level segmentations. We propose a novel approach that relies exclusively on the integration of generic spatio-temporal attention cues. Our strategy, named Multi-Attention Instance Network (MAIN), overcomes challenging segmentation scenarios over arbitrary videos without modelling sequence- or instance-specific knowledge. We design MAIN to segment multiple instances in a single forward pass, and optimize it with a novel loss function that favors class agnostic predictions and assigns instance-specific penalties. We achieve state-of-the-art performance on the challenging Youtube-VOS dataset and benchmark, improving the unseen Jaccard and F-Metric by 6.8% and 12.7% respectively, while operating at real-time (30.3 FPS).
    Sponsors
    This work was partially supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research, and by the German-Colombian Academic Cooperation between the German Research Foundation (DFG grant BR 3815/9-1) and Universidad de los Andes, Colombia.
    Publisher
    arXiv
    arXiv
    1904.05847
    Additional Links
    https://arxiv.org/pdf/1904.05847
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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