Confidence-Guided Dual Stream Hybrid Network for Salient Object Detection

References

[1] A. Borji, M. Cheng, H. Jiang, and J. Li, “Salient Object Detection: A Survey,” Computer Vision and Image Understanding, vol. 147, pp. 92–114, 2016.

[2] R. Achanta, S. Hemami, F. Estrada, and S. Süsstrunk, “Frequency-Tuned Salient Region Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1597–1604, 2009.

[3] M. M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S. M. Hu, “Global Contrast Based Salient Region Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569–582, 2015.

[4] L. Itti, C. Koch, and E. Niebur, “A Model of Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254–1259, 1998.

[5] G. Li and Y. Yu, “Visual Saliency Based on Multiscale Deep Features,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5455–5463, 2015.

[6] Q. Hou, M. Cheng, X. Hu, A. Borji, Z. Tu, and P. Torr, “Deeply Supervised Salient Object Detection With Short Connections,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 4, pp. 815–828, 2019.

[7] N. Liu and J. Han, “DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 678–686, 2016.

[8] L. Wang, H. Lu, X. Ruan, and M. H. Yang, “Deep Networks for Saliency Detection via Local Estimation and Global Search,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3183–3192, 2015.

[9] G. Li and Y. Yu, “Deep Contrast Learning for Salient Object Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 478–487, 2016.

[10] T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H. Shum, “Learning to Detect a Salient Object,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 353–367, 2011.

[11] S. Zhao, X. Li, and Y. Pang, “Pyramid Feature Attention Network for Saliency Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[12] J. Wei, S. Wang, and Q. Huang, “F3Net: Fusion, Feedback, and Focus for Salient Object Detection,” Proc. AAAI Conference on Artificial Intelligence, 2020.

[13] W. Wang, S. Zhao, J. Shen, S. Hoi, and A. Borji, “Salient Object Detection with for Salient Object Detection,” IEEE Pyramid Attention and Salient Edges,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[14] L. Zhang, J. Dai, H. Lu, Y. He, and G. Wang, “A Bi-Directional Message Passing Model for Salient Object Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[15] Z. Wu, L. Su, and Q. Huang, “Stacked Cross Refinement Network for Edge-Aware Salient Object Detection,” Proc. IEEE International Conference on Computer Vision (ICCV), 2019.

[16] X. Zhang, T. Wang, J. Qi, H. Lu, and G. Wang, “Progressive Attention Guided Network for Salient Object Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[17] J. Chen, X. Fu, J. Liu, and Y. Ling, “An Attention Guided Network for Salient Object Detection,” IEEE Transactions on Image Processing, 2020.

[18] K. Fu, D. Fan, G. Ji, and Q. Zhao, “JL-DCF: Joint Learning and Dense Collaborative Fusion for RGB-D Salient Object Detection,” Proc. IEEE Conference on Computer Vision Recognition (CVPR), 2020. and Pattern.

[19] D. Fan, G. Ji, M. Cheng, and L. Shao, “PoolNet: Pooling-Based Salient Object Detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[20] X. Zhao, L. Zhang, H. Lu, and M. H. Yang, “Hierarchical Contextual Attention Transactions on Image Processing, 2020.

[21] D. Fan, M. Cheng, Y. Liu, T. Li, and A. Borji, “Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground,” European Conference on Computer Vision (ECCV), pp. 186–202, 2018.

@article{1715817,
      author = {Dr. B. Hemantha Kumar, Rohitha Maddineni, Karanam Ramyasri, Gogineni Charan Sai, Borra Kasi Naga Sai Lakshmi},
      title = {Confidence-Guided Dual Stream Hybrid Network for Salient Object Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {3204-3211},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715817.pdf},
      abstract = {Salient Object Detection (SOD) aims to automatically identify visually important regions in an image and is considered a fundamental task in computer vision. It plays an important role in various applications such as image segmentation, object recognition, visual tracking, and scene understanding. However, variations in object appearance, complex backgrounds, low contrast between foreground and background, and the presence of multiple objects make automatic saliency detection a challenging problem. Traditional saliency detection methods often fail to effectively capture both global contextual information and fine spatial details, which leads to incomplete object detection and inaccurate saliency maps. To overcome these limitations, this paper proposes a Confidence-Guided Dual-Stream Network for efficient salient object detection. The proposed framework utilizes two parallel feature extraction streams to capture complementary visual representations from input images. In addition, a confidence-guided mechanism evaluates the reliability of extracted features and adaptively adjusts their contributions during feature fusion. Experimental results on benchmark datasets demonstrate that the proposed approach produces more accurate saliency maps and improves salient object localization in complex visual scenes.},
      keywords = {Computer Vision, Confidence-Guided Feature Fusion, Deep Learning, Dual-Stream Network, Image Processing, Salient Object Detection.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715817}
  }