DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization
Abstract
In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization. For this purpose, we annotate semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset, which provides information on object pose, occlusion, viewpoint and functionality. DeePM is a latent graphical model based on the state-of-the-art R-CNN framework, which learns an explicit representation of the object-part configuration with flexible type sharing (e.g., a sideview horse head can be shared by a fully-visible sideview horse and a highly truncated sideview horse with head and neck only). For comparison, we also present an end-to-end Object-Part (OP) R-CNN which learns an implicit feature representation for jointly mapping an image ROI to the object and part bounding boxes. We evaluate the proposed methods for both the object and part detection performance on PASCAL VOC 2012, and show that DeePM consistently outperforms OP R-CNN in detecting objects and parts. In addition, it obtains superior performance to Fast and Faster R-CNNs in object detection.
Cite
@article{arxiv.1511.07131,
title = {DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization},
author = {Jun Zhu and Xianjie Chen and Alan L. Yuille},
journal= {arXiv preprint arXiv:1511.07131},
year = {2016}
}
Comments
the final revision to ICLR 2016, in which some color errors in the figures are fixed