Multiple Context Aggregation Network for Saliency Prediction
Lingtong Meng, Shuai Li, Jiaqi Feng, Yipeng Liu, Ce Zhu · 2019
With the rapid development of deep learning techniques, saliency prediction has been widely studied with deep convolutional neural networks (DCNNs). In this paper, we delve into the feature characteristics that heavily affect saliency prediction, feature scale and feature level, to be specific. It is shown that in addition to high-level features, large-scale and/or low-level features are also important to saliency prediction. Therefore, a dilated asymmetric convolution with large kernel (DACLK) is designed to obtain large-scale features. Then, a global multi-level feature aggregation method (GMLFA) is proposed to incorporate features from shallow layers. GMLFA can increase the scale of the features by down-sampling operations and improve saliency prediction with more large-scale (global) multilevel features. Based on the two modules, a multiple context aggregation network (MCA-Net) is developed for saliency prediction. Experiments demonstrate that MCA-Net outperforms other models on SALICON test dataset and have very competitive results on MIT300 Benchmark.