Training a Convolutional Neural Network (CNN) for semantic segmentation
typically requires to collect a large amount of accurate pixel-level
annotations, a hard and expensive task. In contrast, simple image tags are
easier to gather. With this paper we introduce a novel weakly-supervised
semantic segmentation model able to learn from image labels, and just image
labels. Our model uses the prior knowledge of a network trained for image
recognition, employing these image annotations as an attention mechanism to
identify semantic regions in the images. We then present a methodology that
builds accurate class-specific segmentation masks from these regions, where
neither external objectness nor saliency algorithms are required. We describe
how to incorporate this mask generation strategy into a fully end-to-end
trainable process where the network jointly learns to classify and segment
images. Our experiments on PASCAL VOC 2012 dataset show that exploiting these
generated class-specific masks in conjunction with our novel end-to-end
learning process outperforms several recent weakly-supervised semantic
segmentation methods that use image tags only, and even some models that
leverage additional supervision or training data.

本文介绍了一种使用图像标签进行弱监督语义分割的模型，利用图像标注作为注意机制来识别图像中的语义区域，并描述了如何将此掩模生成策略纳入完全端到端可训练的过程中，其中网络同时学习对图像进行分类和分割，实验表明，利用生成的类特定掩模可以超越只使用图像标记的最新弱监督语义分割方法甚至某些利用额外监督或训练数据的模型。

利用先验网络知识学习弱监督语义分割

Learning to Exploit the Prior Network Knowledge for Weakly-Supervised  Semantic Segmentation

The recent rapid and tremendous success of deep convolutional neural networks
(CNN) on many challenging computer vision tasks largely derives from the
accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
Nevertheless, unsupervised image categorization (i.e., without the ground-truth
labeling) is much less investigated, yet critically important and difficult
when annotations are extremely hard to obtain in the conventional way of
"Google Search" and crowd sourcing. We address this problem by presenting a
looped deep pseudo-task optimization (LDPO) framework for joint mining of deep
CNN features and image labels. Our method is conceptually simple and rests upon
the hypothesized "convergence" of better labels leading to better trained CNN
models which in turn feed more discriminative image representations to
facilitate more meaningful clusters/labels. Our proposed method is validated in
tackling two important applications: 1) Large-scale medical image annotation
has always been a prohibitively expensive and easily-biased task even for
well-trained radiologists. Significantly better image categorization results
are achieved via our proposed approach compared to the previous
state-of-the-art method. 2) Unsupervised scene recognition on representative
and publicly available datasets with our proposed technique is examined. The
LDPO achieves excellent quantitative scene classification results. On the MIT
indoor scene dataset, it attains a clustering accuracy of 75.3%, compared to
the state-of-the-art supervised classification accuracy of 81.0% (when both are
based on the VGG-VD model).

本研究提出一种基于循环伪任务优化的框架，用于联合挖掘深度 CNN 特征和图像标签，并对大规模医学图像注释和无监督场景识别等应用进行了验证。