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

We propose a novel algorithm for weakly supervised semantic segmentation
based on image-level class labels only. In weakly supervised setting, it is
commonly observed that trained model overly focuses on discriminative parts
rather than the entire object area. Our goal is to overcome this limitation
with no additional human intervention by retrieving videos relevant to target
class labels from web repository, and generating segmentation labels from the
retrieved videos to simulate strong supervision for semantic segmentation.
During this process, we take advantage of image classification with
discriminative localization technique to reject false alarms in retrieved
videos and identify relevant spatio-temporal volumes within retrieved videos.
Although the entire procedure does not require any additional supervision, the
segmentation annotations obtained from videos are sufficiently strong to learn
a model for semantic segmentation. The proposed algorithm substantially
outperforms existing methods based on the same level of supervision and is even
as competitive as the approaches relying on extra annotations.

本文提出一种基于图像级别类别标签的弱监督语义分割算法，利用 Web 存储库中有关目标类别标签的视频检索并从检索到的视频中生成分割标签以模拟语义分割的强监督，该算法明显优于现有的同等监督方法，甚至与依赖额外标注方法的方法一样有竞争力。