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Mesh saliency via weakly supervised classification-for-saliency CNN

Song, Ran, Liu, Yonghuai and Rosin, Paul 2021. Mesh saliency via weakly supervised classification-for-saliency CNN. IEEE Transactions on Visualization and Computer Graphics 27 (1) , pp. 151-164. 10.1109/TVCG.2019.2928794

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Abstract

Recently, effort has been made to apply deep learning to the detection of mesh saliency. However, one major barrier is to collect a large amount of vertex-level annotation as saliency ground truth for training the neural networks. Quite a few pilot studies showed that this task is quite difficult. In this work, we solve this problem by developing a novel network trained in a weakly supervised manner. The training is end-to-end and does not require any saliency ground truth but only the class membership of meshes. Our Classification-for-Saliency CNN (CfS-CNN) employs a multi-view setup and contains a newly designed two-channel structure which integrates view-based features of both classification and saliency. It essentially transfers knowledge from 3D object classification to mesh saliency. Our approach significantly outperforms the existing state-of-the-art methods according to extensive experimental results. Also, the CfS-CNN can be directly used for scene saliency. We showcase two novel applications based on scene saliency to demonstrate its utility.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1077-2626
Date of First Compliant Deposit: 15 August 2019
Date of Acceptance: 10 July 2019
Last Modified: 22 Jan 2021 02:29
URI: http://orca.cf.ac.uk/id/eprint/124144

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