A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network
This work addresses model efficiency for RGB-D saliency detection, an incremental improvement in computer vision for applications like object recognition.
The paper tackles the problem of large model sizes in multiscale CNNs for RGB-D saliency detection by proposing a Saliency Enhanced Feature Fusion (SEFF) module, which enhances features using saliency maps to improve fusion and achieves superior performance over ten state-of-the-art detectors on five benchmark datasets.
Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales alwaysresults in large model sizes, impeding the application of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a customized feature fusion module, called Saliency Enhanced Feature Fusion (SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the neighboring scales to enhance the necessary features for fusing, resulting in more representative fused features. Our multiscale RGB-D saliency detector uses SEFF and processes images with three different scales. SEFF is used to fuse the features of RGB and depth images, as well as the features of decoders at different scales. Extensive experiments on five benchmark datasets have demonstrated the superiority of our method over ten SOTA saliency detectors.