Deeply Supervised 3D Recurrent FCN for Salient Object Detection in Videos

Trung-Nghia Le and Akihiro Sugimoto

Abstract

This paper presents a novel end-to-end 3D fully convolutional network for salient object detection in videos. The proposed network uses 3D filters in the spatiotemporal domain to directly learn both spatial and temporal information to have 3D deep features, and transfers the 3D deep features to pixel-level saliency prediction, outputting saliency voxels. In our network, we combine the refinement at each layer and deep supervision to efficiently and accurately detect salient object boundaries. The refinement module recurrently enhances to learn contextual information into the feature map. Applying deeply-supervised learning to hidden layers, on the other hand, improves details of the intermediate saliency voxel, and thus the saliency voxel is refined progressively to become finer and finer. Intensive experiments using publicly available benchmark datasets confirm that our network outperforms state-of-the-art methods.

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DOI

10.5244/C.31.38
https://dx.doi.org/10.5244/C.31.38

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Bibtex

            @inproceedings{BMVC2017_38,
                title={Deeply Supervised 3D Recurrent FCN for Salient Object Detection in Videos},
                author={Trung-Nghia Le and Akihiro Sugimoto},
                year={2017},
                month={September},
                pages={38.1-38.13},
                articleno={38},
                numpages={13},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.38},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.38}
            }