Abstract
In this paper we aim to address the problem of saliency detection on RGB-D image pairs based on a multi-stream late fusion network. With the prevalence of RGB-D sensors, leveraging additional depth information to facilitate saliency detection task has drawn increasing attention. However, the key challenge that how to fuse RGB data and depth data in an optimum manner is still under-studied. Conventional wisdom simply regards depth information as an undifferentiated channel and models RGB-D saliency detection by using existing RGB saliency detection models directly. However, this paradigm is incapable of capturing specific representations in depth modality and also powerless in fusing multi-modal information. In this paper, we address this problem by proposing a simple yet principled late fusion strategy carried out in conjunction with convolutional neural networks (CNNs). The proposed network is able to learn discriminant representations and explore the complementarity between RGB and depth modalities. Comprehensive experiments on two public datasets witness the benefits of the proposed RGB-D saliency detection network.
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This work is funded by the Research Grants Council of Hong Kong (CityU 11205015) and the National Natural Science Foundation of China (NSFC) (61673329).
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Chen, H., Li, Y., Su, D. (2017). RGB-D Saliency Detection by Multi-stream Late Fusion Network. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_41
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DOI: https://doi.org/10.1007/978-3-319-68345-4_41
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