  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.
