The high cost of pixel-level annotations makes it appealing to train saliency
detection models with weak supervision. However, a single weak supervision
source usually does not contain enough information to train a well-performing
model. To this end, we propose a unified framework to train saliency detection
models with diverse weak supervision sources. In this paper, we use category
labels, captions, and unlabelled data for training, yet other supervision
sources can also be plugged into this flexible framework. We design a
classification network (CNet) and a caption generation network (PNet), which
learn to predict object categories and generate captions, respectively,
meanwhile highlight the most important regions for corresponding tasks. An
attention transfer loss is designed to transmit supervision signal between
networks, such that the network designed to be trained with one supervision
source can benefit from another. An attention coherence loss is defined on
unlabelled data to encourage the networks to detect generally salient regions
instead of task-specific regions. We use CNet and PNet to generate pixel-level
pseudo labels to train a saliency prediction network (SNet). During the testing
phases, we only need SNet to predict saliency maps. Experiments demonstrate the
performance of our method compares favourably against unsupervised and weakly
supervised methods and even some supervised methods.

利用分类标签、字幕和未标记数据等不同类型的监督实现显著性检测模型的训练，通过 CNet 和 PNet 生成像素级伪标签进行训练，使用注意力传输损失和注意力一致性损失，使弱监督训练的模型性能得到提升，实验表明我们的方法与许多监督方法相比具有较好的性能。