  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.
