Abstract
The performance of salient object detection has been significantly advanced by using fully convolutional networks (FCN). However, it still remains nontrivial to take full advantage of the multi-level convolutional features for salient object detection. In this paper, a dense grid network framework (denoted \(\mathbf {DGrid}\)) is proposed to solve the above problem, which mainly consists of the backbone module, extended module and fusion module. Specifically, \(\mathbf {DGrid}\) utilizes a multi-branch refinement mechanism for saliency detection. First, the backbone module is used to generate a coarse prediction map. Then, the extended module, which contains four branches, is used to improve the resolution and precision of the prediction map gradually from coarse to fine. Moreover, we proposed the densely connected strategy to fully fuse features at different levels. Finally, the fusion module densely fuses the highest level features of all branches to achieve the final saliency map. Experimental results on five widely used benchmark datasets demonstrate that \(\mathbf {DGrid}\) can improve the accuracy of detection by maintaining a high-resolution feature branch, and it outperforms state-of-the-art approaches without any post-processing.
This work supported by Science and Technology Project of State Grid Fujian Electric Power Co., Ltd. under grant 52130M19000X and National Natural Science Foundation of China (NSFC) under grant 61873067.
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Borji, A., Frintrop, S., Sihite, D.N., et al.: Adaptive object tracking by learning background context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 23–30. IEEE, Piscataway (2012)
Donoser, M., Urschler, M., Hirzer, M., et al.: Saliency driven total variation segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 817–824. IEEE, Piscataway (2009)
Feng, M., Lu, H., Ding, E.: Attentive feedback network for boundary-aware salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1623–1632. IEEE, Piscataway (2019)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Piscataway (2016)
Hou, Q., Cheng, M., Hu, X., et al.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212. IEEE, Piscataway (2017)
Huang, G., Liu, S., Van, D., et al.: CondenseNet: an efficient DenseNet using learned group convolutions. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2752–2761. IEEE, Piscataway (2018)
Jiang, H., Wang, J., Yuan, Z., et al.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090. IEEE, Piscataway (2013)
Lee, G., Tai, Y., Kim, J.: Deep saliency with encoded low level distance map and high level features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 660–668. IEEE, Piscataway (2016)
Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5455–5463. IEEE, Piscataway (2015)
Li, Y., Hou, X., Koch, C., et al.: The secrets of salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280–287. IEEE, Piscataway (2014)
Liu, N., Han, J., Yang, M.H.: PiCANet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3089–3098. IEEE, Piscataway (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE, Piscataway (2015)
Luo, Z., Mishra, A., Achkar, A., et al.: Non-local deep features for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6609–6617. IEEE, Piscataway (2017)
Qin, X., Zhang, Z., Huang, C., et al.: BASNet: boundary-aware salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7479–7489. IEEE, Piscataway (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: IEEE International Conference on Learning Representations, pp. 1–14 (2013)
Wang, L., Lu, H., Wang, Y., et al.: Learning to detect salient objects with imagelevel supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136–145. IEEE, Piscataway (2017)
Wang, T., Borji, A., Zhang, L., et al.: A stagewise refinement model for detecting salient objects in images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4019–4028. IEEE, Piscataway (2017)
Wang, T., Zhang, L., Wang, S., et al.: Detect globally, refine locally: a novel approach to saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3127–3135. IEEE, Piscataway (2018)
Wu, R., Feng, M., Guan, W., et al.: A mutual learning method for salient object detection with intertwined multi-supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8150–8159. IEEE, Piscataway (2019)
Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3907–391. IEEE, Piscataway (2019)
Yan, Q., Xu, L., Shi, J., et al.: Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162. IEEE, Piscataway (2013)
Yang, C., Zhang, L., Lu, H., et al.: saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173. IEEE, Piscataway (2013)
Zhang, L., Dai, J., Lu, H., et al.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741–1750. IEEE, Piscataway (2018)
Zhang, X., Wang, T., Qi, J., et al.: Progressive attention guided recurrent network for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 714–722. Piscataway, NJ:IEEE (2018)
Zhu, W., Liang, S., Wei, Y., et al.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821. IEEE, Piscataway (2014)
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Cai, Y. et al. (2022). DGrid: Dense Grid Network for Salient Object Detection. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_18
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