First published: 2016/05/30 (5 years ago) Abstract: While convolutional neural networks have gained impressive success recently
in solving structured prediction problems such as semantic segmentation, it
remains a challenge to differentiate individual object instances in the scene.
Instance segmentation is very important in a variety of applications, such as
autonomous driving, image captioning, and visual question answering. Techniques
that combine large graphical models with low-level vision have been proposed to
address this problem; however, we propose an end-to-end recurrent neural
network (RNN) architecture with an attention mechanism to model a human-like
counting process, and produce detailed instance segmentations. The network is
jointly trained to sequentially produce regions of interest as well as a
dominant object segmentation within each region. The proposed model achieves
state-of-the-art results on the CVPPP leaf segmentation dataset and KITTI
vehicle segmentation dataset.