Abstract
Salient object detection (SOD) aims to find the most attractive object(s) in a scene. In recent years, SOD methods based on deep learning have become the mainstream. Existing methods mostly aggregate the multi-level features extracted by convolutional neural network (CNN) to model the object, or refine the boundary details of the object through multi-scale feature fusion. In this paper, the paradigm of approximately decoupled component supervision is proposed for SOD. Our insight is that the attractive performance of SOD requires explicit modeling of the body and edge of the object with different supervisions. Specifically, we first capture image features through foreground attention (FA) mechanism, cross-block semantic correlation aggregation (CBSC), and resolution-based feature integration (RFI) to make object parts more consistent and complete. Then the detailed edge is obtained by subtracting the body part from the complete mask. By explicitly sampling the body and edge pixels of the salient object, we further optimize the resulting body features and the residual edge features under the supervision of approximate decoupling. Benefiting from the abundant edge information and accurate location information, the framework with various backbone proposed by us can achieve better internal consistency and accurate boundaries of the object. Experimental results on six widely used benchmark datasets demonstrate the superiority and competitiveness of our approach in terms of four popular evaluation metrics. Moreover, the proposed method is an end-to-end saliency detection network without any pre-processing or post-processing.
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References
He J, Feng F, Liu X, Cheng T, Lin T, Chung H, Chang S (2012) Mobile product search with bag of hash bits and boundary reranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3005–3012
Liu G, Fan D (2013) A model of visual attention for natural image retrieval. In: Proceedings of the IEEE Conference on information science and cloud computing companion, pp 728–733
Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network. In: International conference on machine learning
Zhang D, Meng D, Zhao L, Han J (2016) Bridging saliency detection to weakly supervised object detection based on selfpaced curriculum learning. In: International joint conferences on artificial intelligence
Zhang S, He F (2020) DRCDN: Learning deep residual convolutional dehazing network. The Visual Computer 36:1797–1808
Chen Y, He F, Li H, Zhang D, Wu Y (2020) A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration. Applied Soft Computing 93:1–9
Jia F, Guan J, Qi S (2020) A mix-supervised unified framework for salient object detection. Applied Intelligence 50:2945–2958
Jiao J, Xue H, Ding J (2021) Non-local duplicate pooling network for salient object detection. Applied Intelligence
Liu J, Hou Q, Cheng M, Feng J, Jiang J (2019) A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3917–3926
Zhao X, Pang Y, Zhang L, Lu H, Zhang L (2020) Suppress and balance: A simple gated network for salient object detection. In: Proceedings of the european conference on computer vision
Feng M, Lu H, Ding E (2019) Attentive feedback network for boundary-aware salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1623–1632
Zhang P, Wang D, Lu H, Wang H, Ruan X (2017) Amulet: aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE international conference on computer vision, pp 202–211
Wang B, Chen Q, Zhou M, Zhang Z, Jin X, Gai K (2020) Progressive feature polishing network for salient object detection. In: Processings of the international joint conference on artificial intelligence
Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagersand M (2019) BASNet: boundary-aware salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7479–7489
Zhang L, Dai J, Lu H, He Y, Wang G (2018) A bi-directional message passing model for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1741–1750
Zhao T, Wu X (2019) Pyramid feature attention network for saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Li T, Song H, Zhang K, Liu Q (2020) Learning residual refinement network with semantic context representation for real-time saliency object detection. Pattern Recognition
Wang T, Zhang L, Wang S, Lu H, Yang G, Ruan X, Borji A (2018) Detect globally, re-fine locally: a novel approach to saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3127–3135
Liu N, Han J, Yang M (2018) Picanet: Learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3089–3098
Zhang L, Zhang J, Lin Z, Lu H, He Y (2019) CapSal: Leveraging captioning to boost semantics for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6024–6033
Chen Z, Xu Q, Cong R, Huang Q (2020) Global context-aware progressive aggregation network for salient object detection. In: Proceedings of the international joint conference on artificial intelligence
Li X, Li X, Zhang L, Cheng G, Shi J (2020) Improving semantic segmentation via decoupled body and edge supervision. In: Proceedings of the european conference on computer vision
Wei J, Wang S, Wu Z, Su C, Huang Q, Tian Q (2020) Label decoupling framework for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Wang W, Lai Q, Fu H, Shen J, Ling H, Yang R (2021) Salient object detection in the deep learning era: an in-depth survey. IEEE Trans Pattern Anal Mach Intell
Fan D, Zhang J, Xu G, Cheng M-M, Shao L (2021) Salient objects in clutter. IEEE Trans Pattern Anal Mach Intell
Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision, pp 1395–1403
Hou Q, Cheng M, Hu X, Borji A, Tu Z, Torr P (2017) Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3203–3212
Wu Z, Su L, Huang Q (2019) Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3907–3916
Wei J, Wang S, Huang Q (2020) \({\text F}^{3}Net\): Fusion, feedback and focus for salient object detection. In: Proceedings of the 34th international joint conference on artificial intelligence
Wang T, Borji A, Zhang L, Zhang P, Lu H (2017) A stage-wise refinement model for detecting salient objects in images. In: Proceedings of the IEEE international conference on computer vision, pp 4039–4048
Zhang X, Wang T, Qi J, Lu H, Wang G (2018) Progressive attention guided recurrent network for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 714–722
Wu Z, Su L, Huang Q (2019) Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of the IEEE international conference on computer vision, pp 7263–7273
Wang X, Ma H, Chen X (2016) Salient object detection via fast r-cnn and low-level cues. In: International conference on image processing, pp 1042–1046
Wang X, Ma H, Chen X, You S (2018) Edge preserving and multi-scale contextual neural network for salient object detection. IEEE Transactions on image processing 27:121–134
Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision
Luo Z, Mishra A, Achkar A, Eichel J, Li S, Jodoin P (2017) Non-local deep features for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Zhao J, Liu J, Fan D, Cao Y, Yang J, Cheng M (2019) EGNet: Edge guidance network for salient object detection. In: Proceedings of the IEEE international conference on computer vision
Jake B (2006) Notes on convolutional neural network. In: neural nets
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141
Woo S, Park J, Lee J, Kweon I (2018) CBAM: Convolutional block attention module. In: Proceedings of the european conference on computer vision
Su J, Li J, Zhang Y, Xia C, Tian Y (2019) Selectivity or Invariance: Boundary-aware salient object detection. In: Proceedings of the IEEE international conference on computer vision
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 770–778
Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1097–1105
Movahedi V, Elder J (2010) Design and perceptual validation of performance measures for salient object segmentation. In: 2010 IEEE Computer society conference on computer vision and pattern recognition-workshops (CVPR-Workshops), pp 49–56
Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1155–1162
Yang C, Zhang L, Lu H, Ruan X, Yang M (2013) Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3166-3173
Li G, Yu Y (2015) Visual saliency based on multiscale deep features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5455-5463
Li Y, Hou X, Koch C, Rehg J, Yuille A (2014) The secrets of salient object segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 280–287
Wang L, Lu H, Wang Y, Feng M, Wang D, Yin B, Ruan X (2017) Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 136–145
Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1597–1604
Perazzi F, Krahenbuhl P, Pritch Y, Hornung A (2012) Saliency filters: Contrast based filtering for salient region detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733–740
Fan D, Cheng M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4548–4557
Deng J, Dong W, Socher R, Li L.-J, Li K, and Li F.-F (2009) Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 248–255
Sun Y, Chen G, Zhou T, Zhang Y, Liu N (2021) Context-aware cross-level fusion network for camouflaged object detection. In: Processings of the international joint conference on artificial intelligence
Zhang P, Wang D, Lu H, Wang H, Yin B (2017) Learning uncertain convolutional features for accurate saliency detection. In: Proceedings of the IEEE international conference on computer vision, pp 212–221
Li X, Yang F, Cheng H, Liu W, Shen D (2018) Contour knowledge transfer for salient object detection. In: Proceedings of the european conference on computer vision, pp 370–385
Wu R, Feng M, Guan W, Wang D, Lu H, Ding E (2019) A mutual learning method for salient object detection with intertwined multi-supervision. In: Proceedings of the IEEE international conference on computer vision
Pang Y, Zhao X, Zhang L, Lu H (2020) Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Gao S, Tan Y, Cheng M, Lu C, Chen Y, Yan S (2020) Highly efficient salient object detection with 100k parameters. In: Proceedings european conference on computer vision
Zhou H, Xie X, Lai J, Chen Z, Yang L (2020) Interactive two-stream decoder for accurate and fast saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9141–9150
Feng M, Lu H, Yu Y (2020) Residual learning for salient object detection. IEEE Transactions on Image Processing 29:4696–4708
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This work was supported in part by the National Natural Science Foundation of China under Grant (No. 61872164).
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Liang, Y., Qin, G., Sun, M. et al. Approximately decoupled component supervision for salient object detection. Appl Intell 52, 16117–16137 (2022). https://doi.org/10.1007/s10489-021-03046-2
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DOI: https://doi.org/10.1007/s10489-021-03046-2