Weakly Supervised Object Localization (WSOL) techniques learn the object
location only using image-level labels, without location annotations. A common
limitation for these techniques is that they cover only the most discriminative
part of the object, not the entire object. To address this problem, we propose
an Attention-based Dropout Layer (ADL), which utilizes the self-attention
mechanism to process the feature maps of the model. The proposed method is
composed of two key components: 1) hiding the most discriminative part from the
model for capturing the integral extent of object, and 2) highlighting the
informative region for improving the recognition power of the model. Based on
extensive experiments, we demonstrate that the proposed method is effective to
improve the accuracy of WSOL, achieving a new state-of-the-art localization
accuracy in CUB-200-2011 dataset. We also show that the proposed method is much
more efficient in terms of both parameter and computation overheads than
existing techniques.

本文提出了一种注意力分布特征 (ADL) 的弱监督目标定位技术，利用自注意机制对模型的特征图进行处理，旨在实现对目标的完整覆盖和信息突出。实验证明该方法有效提高了目标识别率和检测精度，在 CUB-200-2011 数据集中实现了最新的定位精度记录。

基于注意力机制的弱监督目标定位的 Dropout 层

Attention-based Dropout Layer for Weakly Supervised Object Localization

Discriminative localization is essential for fine-grained image
classification task, which devotes to recognizing hundreds of subcategories in
the same basic-level category. Reflecting on discriminative regions of objects,
key differences among different subcategories are subtle and local. Existing
methods generally adopt a two-stage learning framework: The first stage is to
localize the discriminative regions of objects, and the second is to encode the
discriminative features for training classifiers. However, these methods
generally have two limitations: (1) Separation of the two-stage learning is
time-consuming. (2) Dependence on object and parts annotations for
discriminative localization learning leads to heavily labor-consuming labeling.
It is highly challenging to address these two important limitations
simultaneously. Existing methods only focus on one of them. Therefore, this
paper proposes the discriminative localization approach via saliency-guided
Faster R-CNN to address the above two limitations at the same time, and our
main novelties and advantages are: (1) End-to-end network based on Faster R-CNN
is designed to simultaneously localize discriminative regions and encode
discriminative features, which accelerates classification speed. (2)
Saliency-guided localization learning is proposed to localize the
discriminative region automatically, avoiding labor-consuming labeling. Both
are jointly employed to simultaneously accelerate classification speed and
eliminate dependence on object and parts annotations. Comparing with the
state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach
achieves both the best classification accuracy and efficiency.

本文提出了一种基于注意力引导下的快速卷积神经网络的判别定位方法，具备同时定位判别区域和编码判别特征的能力，并将其应用于细粒度图像分类任务中，取得了最佳分类准确性和效率。

基于显著性引导的快速 R-CNN 细粒度判别定位

Fine-grained Discriminative Localization via Saliency-guided Faster  R-CNN

A key challenge in fine-grained recognition is how to find and represent
discriminative local regions. Recent attention models are capable of learning
discriminative region localizers only from category labels with reinforcement
learning. However, not utilizing any explicit part information, they are not
able to accurately find multiple distinctive regions. In this work, we
introduce an attribute-guided attention localization scheme where the local
region localizers are learned under the guidance of part attribute
descriptions. By designing a novel reward strategy, we are able to learn to
locate regions that are spatially and semantically distinctive with
reinforcement learning algorithm. The attribute labeling requirement of the
scheme is more amenable than the accurate part location annotation required by
traditional part-based fine-grained recognition methods. Experimental results
on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme
on both fine-grained recognition and attribute recognition.

该研究通过属性指导下的注意力定位方案设计新的奖励策略，使用强化学习算法定位具有空间和语义区别的局部特征区域，从而在精细识别和属性识别方面超越了传统基于部件的方法。