Abstract
Salient object detection aims to obtain the most attractive objects from the input images, which severs as a pre-processing step for many image processing tasks. This paper presents a novel deep neural network design for salient object detection by formulating a pyramid spatial context module, PSC module for short, to capture the spatial context information at multiple scales. To achieve this, we first adopt convolutional operations with different dilated rates to generate the feature maps with different respective fields, and then use the two-round recurrent translations to explore multiple types of spatial context features on these feature maps. By further inserting this module in a deep network, namely PSCNet, we are able to optimize the network in an end-to-end manner for salient object detection. We evaluate the proposed method on six public benchmark datasets by comparing it with 25 salient object detection methods. The experimental results demonstrate that our PSCNet performs favorably against all the other methods.
Highlights
Saliency detection is one of useful pre-processing steps for lots of image processing tasks, including video compression [1], video abstraction [2], image editing [3], texture smoothing [4], object detection [5], [6], and few-shot learning [7]
NETWORK OVERVIEW Figure 2 illustrates the overall architecture of our pyramid spatial context network (PSCNet), which uses a single image as the input and outputs a predicted saliency map in an endto-end manner
After obtaining the convolutional features, we leverage the proposed pyramid spatial context (PSC) module to aggregate multi-level context features, combine the context features from different levels and from the original convolutional features, and use a 1×1 convolution to reduce the number of feature channels and obtain Fo
Summary
Saliency detection is one of useful pre-processing steps for lots of image processing tasks, including video compression [1], video abstraction [2], image editing [3], texture smoothing [4], object detection [5], [6], and few-shot learning [7]. It aims to find the most obvious objects from the input image and this task has been widely studied in the past years. These context features are obtained by propagating the information from
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