Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation

Shu Liu; Xiaojuan Qi; Jianping Shi; Hong Zhang; Jiaya Jia

CVPR 2016oral110 citations

Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation

Shu Liu, Xiaojuan Qi, Jianping Shi, Hong Zhang, Jiaya Jia

Abstract

Aiming at simultaneous detection and segmentation (SDS), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method benefits from end-to-end training. Without object proposal generation, computation time can also be reduced. In experiments, our method yields results 62.1% and 61.8% in terms of mAPr on VOC2012 segmentation val and VOC2012 SDS val, which are state-of-the-art at the time of submission. We also report results on Microsoft COCO test-std/test-dev dataset in this paper.

BibTeX

@inproceedings{cvpr2016_multiscalepatcha,
  title = {Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation},
  author = {Shu Liu and Xiaojuan Qi and Jianping Shi and Hong Zhang and Jiaya Jia},
  booktitle = {CVPR 2016},
  year = {2016}
}