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

FastMask: Segment Multi-scale Object Candidates in One Shot