Jifeng Dai, Kaiming He, Yi Li, Shaoqing Ren, Jian Sun
Fully convolutional networks (FCNs) have been proven very successful for semantic segmentation, but the FCN outputs are unaware of object instances. In this paper, we develop FCNs that are capable of proposing instance-level segment candidates. In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances. On top of these instance-sensitive score maps, a simple assembling module is able to output instance candidate at each position. In contrast to the recent DeepMask method for segmenting instances, our method does not have any high-dimensional layer related to the mask resolution, but instead exploits image local coherence for estimating instances. We present competitive results of instance segment proposal on both PASCAL VOC and MS COCO.
这篇工作又名InstanceFCN。实例分割方面，由于网络难以同时进行分类和分割任务，因此首先流行的是二阶段实例分割网络，首先对输入找到实例的proposal，然后在其中进行密集预测（也就是先框框再分割）。本文从名称上看不是一篇讲实例分割的文章，是讲如何通过FCN获得实例级别的分割mask的的。
在阅读之前我想提醒一下，这篇工作的效果是比较差的，毕竟是早期工作。不过这篇工作具有不错的启发意义，值得读一读。后面的一篇工作FCIS（Fully Convolutional Instance-aware Semantic Segmentation）中就借鉴了本文中提出的instance-sensitive score maps（请不要弄混本篇工作和FCIS）。本文的一大贡献就是提出使用instance-sensitive score maps区分不同个体。