Weakly supervised object detection (WSOD), which is the problem of learning
detectors using only image-level labels, has been attracting more and more
interest. However, this problem is quite challenging due to the lack of
location supervision. To address this issue, this paper integrates saliency
into a deep architecture, in which the location in- formation is explored both
explicitly and implicitly. Specifically, we select highly confident object pro-
posals under the guidance of class-specific saliency maps. The location
information, together with semantic and saliency information, of the selected
proposals are then used to explicitly supervise the network by imposing two
additional losses. Meanwhile, a saliency prediction sub-network is built in the
architecture. The prediction results are used to implicitly guide the
localization procedure. The entire network is trained end-to-end. Experiments
on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts.

本文主要研究弱监督物体检测（WSOD）中缺乏位置监督的问题，并将显著性集成到深度网络中，对高置信对象提出类别特定的显著性，这些特定的位置信息、语义信息和显著信息来显式监督网络，并通过预测网络隐式地指导本地化过程，实现了端到端的训练，实验结果表明，我们的方法优于所有最先进的方法。

基于显著性引导的弱监督物体检测的端到端学习

Saliency Guided End-to-End Learning for Weakly Supervised Object  Detection

Saliency integration has attracted much attention on unifying saliency maps
from multiple saliency models. Previous offline integration methods usually
face two challenges: 1. if most of the candidate saliency models misjudge the
saliency on an image, the integration result will lean heavily on those
inferior candidate models; 2. an unawareness of the ground truth saliency
labels brings difficulty in estimating the expertise of each candidate model.
To address these problems, in this paper, we propose an arbitrator model (AM)
for saliency integration. Firstly, we incorporate the consensus of multiple
saliency models and the external knowledge into a reference map to effectively
rectify the misleading by candidate models. Secondly, our quest for ways of
estimating the expertise of the saliency models without ground truth labels
gives rise to two distinct online model-expertise estimation methods. Finally,
we derive a Bayesian integration framework to reconcile the saliency models of
varying expertise and the reference map. To extensively evaluate the proposed
AM model, we test twenty-seven state-of-the-art saliency models, covering both
traditional and deep learning ones, on various combinations over four datasets.
The evaluation results show that the AM model improves the performance
substantially compared to the existing state-of-the-art integration methods,
regardless of the chosen candidate saliency models.

本研究提出一种基于仲裁模型的显著性整合方法，通过参考显著性模型和外部知识来纠正误导，利用两种不同的模型专业度估计方法，并使用贝叶斯整合框架，在四个数据集上对 27 种状态下的显著性模型进行广泛评估，结果表明提出的方法较现有的最先进的整合方法显著提高了性能。