Multi scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation
Aiming at simultaneous detection and segmentation (SD S) 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.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)