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arXiv · 1612.08843

FastMask: Segment Multi-scale Object Candidates in One Shot

Hexiang Hu·Shiyi Lan·Yuning Jiang·Zhimin Cao·Fei Sha
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Abstract

Objects appear to scale differently in natural images. This fact requires methods dealing with object-centric tasks (e.g. object proposal) to have robust performance over variances in object scales. In the paper, we present a novel segment proposal framework, namely FastMask, which takes advantage of hierarchical features in deep convolutional neural networks to segment multi-scale objects in one shot. Innovatively, we adapt segment proposal network into three different functional components (body, neck and head). We further propose a weight-shared residual neck module as well as a scale-tolerant attentional head module for efficient one-shot inference. On MS COCO benchmark, the proposed FastMask outperforms all state-of-the-art segment proposal methods in average recall being 2~5 times faster. Moreover, with a slight trade-off in accuracy, FastMask can segment objects in near real time (~13 fps) with 800*600 resolution images, demonstrating its potential in practical applications. Our implementation is available on https://github.com/voidrank/FastMask.

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Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha. 2016-12-28. FastMask: Segment Multi-scale Object Candidates in One Shot. https://arxiv.org/abs/1612.08843

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