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.

本文提出了一种深度基于部件的模型（DeePM），旨在实现符号对象检测和语义部位定位，通过 PASCAL VOC 2012 数据集上的 20 种物体类别的语义部分注释来完成。DeePM 是基于最先进的 R-CNN 框架的潜在图形模型，通过灵活的类型共享学习对象 - 部分配置的显式表示。我们评估了所提出的方法在 PASCAL VOC 2012 上的对象和部分检测性能，并表明 DeePM 在检测对象和部件方面始终优于 OP R-CNN，并且在对象检测方面获得了比 Fast 和 Faster R-CNN 更好的性能。

DeePM: 一种用于目标检测和语义部件定位的深度基于部件模型

DeePM: A Deep Part-Based Model for Object Detection and Semantic Part  Localization

This work explores the use of spatial context as a source of free and
plentiful supervisory signal for training a rich visual representation. Given
only a large, unlabeled image collection, we extract random pairs of patches
from each image and train a convolutional neural net to predict the position of
the second patch relative to the first. We argue that doing well on this task
requires the model to learn to recognize objects and their parts. We
demonstrate that the feature representation learned using this within-image
context indeed captures visual similarity across images. For example, this
representation allows us to perform unsupervised visual discovery of objects
like cats, people, and even birds from the Pascal VOC 2011 detection dataset.
Furthermore, we show that the learned ConvNet can be used in the R-CNN
framework and provides a significant boost over a randomly-initialized ConvNet,
resulting in state-of-the-art performance among algorithms which use only
Pascal-provided training set annotations.

该研究探讨了利用空间上下文作为丰富的视觉表征的监督信号的方法。通过从每个图像中提取随机配对的路径来训练卷积神经网络，用于预测第二个路径相对于第一个路径的位置，从而实现对目标的识别，并证明该方法在性能上优于其他算法。