Weakly supervised object localization(WSOL) remains an open problem given the
deficiency of finding object extent information using a classification network.
Although prior works struggled to localize objects through various spatial
regularization strategies, we argue that how to extract object structural
information from the trained classification network is neglected. In this
paper, we propose a two-stage approach, termed structure-preserving activation
(SPA), toward fully leveraging the structure information incorporated in
convolutional features for WSOL. First, a restricted activation module (RAM) is
designed to alleviate the structure-missing issue caused by the classification
network on the basis of the observation that the unbounded classification map
and global average pooling layer drive the network to focus only on object
parts. Second, we designed a post-process approach, termed self-correlation map
generating (SCG) module to obtain structure-preserving localization maps on the
basis of the activation maps acquired from the first stage. Specifically, we
utilize the high-order self-correlation (HSC) to extract the inherent
structural information retained in the learned model and then aggregate HSC of
multiple points for precise object localization. Extensive experiments on two
publicly available benchmarks including CUB-200-2011 and ILSVRC show that the
proposed SPA achieves substantial and consistent performance gains compared
with baseline approaches.Code and models are available at
this https URL

本文提出了一种名为 SPA 的两阶段方法，通过受限激活模块（RAM）和自相关图生成模块（SCG）的设计，从分类网络中提取对象结构信息，以实现弱监督对象定位（WSOL），在 CUB-200-2011 和 ILSVRC 两个公共基准测试中，SPA 相对于基准方法有着显著和一致的性能提升。