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Fast Video Object Segmentation Using the Global Context Module

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12355)

Abstract

We developed a real-time, high-quality semi-supervised video object segmentation algorithm. Its accuracy is on par with the most accurate, time-consuming online-learning model, while its speed is similar to the fastest template-matching method with sub-optimal accuracy. The core component of the model is a novel global context module that effectively summarizes and propagates information through the entire video. Compared to previous approaches that only use one frame or a few frames to guide the segmentation of the current frame, the global context module uses all past frames. Unlike the previous state-of-the-art space-time memory network that caches a memory at each spatio-temporal position, the global context module uses a fixed-size feature representation. Therefore, it uses constant memory regardless of the video length and costs substantially less memory and computation. With the novel module, our model achieves top performance on standard benchmarks at a real-time speed.

Keywords

Video object segmentation Global context module 

Supplementary material

504449_1_En_43_MOESM1_ESM.pdf (152 kb)
Supplementary material 1 (pdf 151 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Applied Research Center (ARC), Tencent PCGShenzhenChina
  2. 2.The University of Hong KongPokfulamHong Kong

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