This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile
Amulet) for salient object detection. The Agile Amulet builds on previous works
to predict saliency maps using multi-level convolutional features. Compared to
previous works, Agile Amulet employs some key innovations to improve training
and testing speed while also increase prediction accuracy. More specifically,
we first introduce a contextual attention module that can rapidly highlight
most salient objects or regions with contextual pyramids. Thus, it effectively
guides the learning of low-layer convolutional features and tells the backbone
network where to look. The contextual attention module is a fully convolutional
mechanism that simultaneously learns complementary features and predicts
saliency scores at each pixel. In addition, we propose a novel method to
aggregate multi-level deep convolutional features. As a result, we are able to
use the integrated side-output features of pre-trained convolutional networks
alone, which significantly reduces the model parameters leading to a model size
of 67 MB, about half of Amulet. Compared to other deep learning based saliency
methods, Agile Amulet is of much lighter-weight, runs faster (30 fps in
real-time) and achieves higher performance on seven public benchmarks in terms
of both quantitative and qualitative evaluation.

本文介绍了一种名为敏捷 Amulet 的多级特征聚合框架，可用于显著性目标检测。敏捷 Amulet 通过多级卷积特征预测显著性地图。相比于之前的研究，敏捷 Amulet 提出了一些关键创新，提高了训练和测试的速度，同时提高了预测的准确度。最终，与其他深度学习方法相比，敏捷 Amulet 在多个公共基准测试中实现了更高的性能和更快的运行速度。