  Predicting salient regions in natural images requires the detection of
objects that are present in a scene. To develop robust representations for this
challenging task, high-level visual features at multiple spatial scales must be
extracted and augmented with contextual information. However, existing models
aimed at explaining human fixation maps do not incorporate such a mechanism
explicitly. Here we propose an approach based on a convolutional neural network
pre-trained on a large-scale image classification task. The architecture forms
an encoder-decoder structure and includes a module with multiple convolutional
layers at different dilation rates to capture multi-scale features in parallel.
Moreover, we combine the resulting representations with global scene
information for accurately predicting visual saliency. Our model achieves
competitive and consistent results across multiple evaluation metrics on two
public saliency benchmarks and we demonstrate the effectiveness of the
suggested approach on five datasets and selected examples. Compared to state of
the art approaches, the network is based on a lightweight image classification
backbone and hence presents a suitable choice for applications with limited
computational resources, such as (virtual) robotic systems, to estimate human
fixations across complex natural scenes.
