Stereo Saliency Detection by Modeling Concatenation Cost Volume Feature
Peipei Song, Jing Zhang, Piotr Koniusz, Nick M. Barnes · 2022
RGB-D image pair based salient object detection models aim to localize the salient objects in an RGB image with extra depth information about the scene provided to guide the detection process. The conventional practice for this task involves explicitly using depth as input to achieve multi-modal learning. In this paper, we observe two main issues within existing RGB-D saliency detection frameworks. Firstly, we claim that it is better to define depth as extra prior information instead of as a part of the input for RGB-D saliency detection, as we can directly perform saliency detection based only on the appearance information from the RGB image, while we cannot perform saliency detection given only the depth data. Secondly, there exists a huge domain gap in terms of the source of depth between different benchmark testing datasets, e.g. depth from Kinect and stereo cameras. In this paper, we focus on the variant of stereo image pair based saliency detection, where the depth is “implicitly” encoded in the stereo image pair for effective RGB-D saliency detection. Experimental results illustrate the effectiveness of our solution.