Weakly supervised object localization (WSOL) aims to localize objects using
only image-level labels. Recently a new paradigm has emerged by generating a
foreground prediction map (FPM) to achieve localization task. Existing
FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction
map and to guide the learning of generator. We argue for using activation value
to achieve more efficient learning. It is based on the experimental observation
that, for a trained network, CE converges to zero when the foreground mask
covers only part of the object region. While activation value increases until
the mask expands to the object boundary, which indicates that more object areas
can be learned by using activation value. In this paper, we propose a
Background Activation Suppression (BAS) method. Specifically, an Activation Map
Constraint module (AMC) is designed to facilitate the learning of generator by
suppressing the background activation value. Meanwhile, by using the foreground
region guidance and the area constraint, BAS can learn the whole region of the
object. In the inference phase, we consider the prediction maps of different
categories together to obtain the final localization results. Extensive
experiments show that BAS achieves significant and consistent improvement over
the baseline methods on the CUB-200-2011 and ILSVRC datasets. Code and models
are available at this https URL

本篇论文提出一种 Background Activation Suppression（简称 BAS）的方法来实现弱监督对象定位（WSOL）任务，在该方法中使用的前景预测图（FPM）和 Activation Map Constraint module（AMC）可从多个方面优化定位结果。实验证明，与以往方法相比，BAS 在 CUB-200-2011 和 ILSVRC 数据集上均达到了显著的改进。

弱监督物体定位的背景激活抑制

Background Activation Suppression for Weakly Supervised Object  Localization

We present a practical method for protecting data during the inference phase
of deep learning based on bipartite topology threat modeling and an interactive
adversarial deep network construction. We term this approach \emph{Privacy
Partitioning}. In the proposed framework, we split the machine learning models
and deploy a few layers into users' local devices, and the rest of the layers
into a remote server. We propose an approach to protect user's data during the
inference phase, while still achieve good classification accuracy.
We conduct an experimental evaluation of this approach on benchmark datasets
of three computer vision tasks. The experimental results indicate that this
approach can be used to significantly attenuate the capacity for an adversary
with access to the state-of-the-art deep network's intermediate states to learn
privacy-sensitive inputs to the network. For example, we demonstrate that our
approach can prevent attackers from inferring the private attributes such as
gender from the Face image dataset without sacrificing the classification
accuracy of the original machine learning task such as Face Identification.

提出了一种名为 “Privacy Partitioning” 的方法，通过将机器学习模型拆分并部署到本地设备和远程服务器中，保护用户数据，防止对状态最先进的深度网络的中间状态进行攻击并推断出隐私敏感的输入。（该方法）能够在不影响分类准确性的情况下有效削弱攻击者获得隐私敏感信息的能力。