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 effi-
cient 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