Page 332 - 《软件学报》2024年第4期

1910 软件学报 2024 年第 35 卷第 4 期

atrous convolution, and fully connected CRFs. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834–848. [doi: 10.
1109/TPAMI.2017.2699184]
[28] Li CY, Cong RM, Piao YR, Xu QQ, Loy CC. RGB-D salient object detection with cross-modality modulation and selection. In: Proc. of
the 16th European Conf. on Computer Vision (ECCV). Glasgow: Springer, 2020. 225–241. [doi: 10.1007/978-3-030-58598-3_14]
3
[29] Deng ZJ, Hu XW, Zhu L, Xu XM, Qin J, Han GQ, Heng PA. R Net: Recurrent residual refinement network for saliency detection. In:
Proc. of the 27th Int’l Joint Conf. on Artificial Intelligence (IJCAI). Stockholm: AAAI, 2018. 684–690.
[30] Wang TT, Borji A, Zhang LH, Zhang PP, Lu HC. A stagewise refinement model for detecting salient objects in images. In: Proc. of the
2017 IEEE Int’l Conf. on Computer Vision (ICCV). Venice: IEEE, 2017. 4039–4048. [doi: 10.1109/ICCV.2017.433]
[31] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. In: Proc. of the
31st Neural Information Processing Systems (NIPS). Long Beach: Curran Associates Inc., 2017. 6000–6010.
2
[32] Qin XB, Zhang ZC, Huang CY, Dehghan M, Zaiane OR, Jagersand M. U -Net: Going deeper with nested U-structure for salient object
detection. Pattern Recognition, 2020, 106: 107404. [doi: 10.1016/j.patcog.2020.107404]
[33] Tang ZQ, Peng X, Geng SJ, Wu LF, Zhang ST, Metaxas D. Quantized densely connected U-nets for efficient landmark localization. In:
Proc. of the 15th European Conf. on Computer Vision (ECCV). Munich: Springer, 2018. 348–364. [doi: 10.1007/978-3-030-01219-9_21]
[34] Li GB, Yu YZ. Visual saliency based on multiscale deep features. In: Proc. of the IEEE Conf. on Computer Vision and Pattern
Recognition (CVPR). Boston: IEEE, 2015. 5455–5463. [doi: 10.1109/CVPR.2015.7299184]
[35] Li GB, Yu YZ. Visual saliency detection based on multiscale deep CNN features. IEEE Trans. on Image Processing, 2016, 25(11):
5012–5024. [doi: 10.1109/TIP.2016.2602079]
[36] Wang LJ, Lu HC, Ruan X, Yang MH. Deep networks for saliency detection via local estimation and global search. In: Proc. of the 2015
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015. 3183–3192. [doi: 10.1109/CVPR.2015.7298938]
[37] Zhao R, Ouyang WL, Li HS, Wang XG. Saliency detection by multi-context deep learning. In: Proc. of the 2015 IEEE Conf. on
Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015. 1265–1274. [doi: 10.1109/CVPR.2015.7298731]
[38] Li X, Zhao LM, Wei LN, Yang MH, Wu F, Zhuang YT, Ling HB, Wang JD. DeepSaliency: Multi-task deep neural network model for
salient object detection. IEEE Trans. on Image Processing, 2016, 25(8): 3919–3930. [doi: 10.1109/TIP.2016.2579306]
[39] Wang WG, Shen JB, Jia YD. Review of visual attention detection. Ruan Jian Xue Bao/Journal of Software, 2019, 30(2): 416–439 (in
Chinese with English abstract). http://www.jos.org.cn/1000-9825/5636.htm [doi: 10.13328/j.cnki.jos.005636]
[40] Zhao XQ, Pang YW, Zhang LH, Lu HC, Ruan X. Self-supervised pretraining for RGB-D salient object detection. In: Proc. of the 36th
AAAI Conf. Artificial Intelligence (AAAI). AAAI, 2022. 3463–3471. [doi: 10.1609/aaai.v36i3.20257]
[41] Qu LQ, He SF, Zhang JW, Tian JD, Tang YD, Yang QX. RGBD salient object detection via deep fusion. IEEE Trans. on Image
Processing, 2017, 26(5): 2274–2285. [doi: 10.1109/TIP.2017.2682981]
[42] Cong RM, Lei JJ, Fu HZ, Huang QM, Cao XC, Hou CP. Co-saliency detection for RGBD images based on multi-constraint feature
matching and cross label propagation. IEEE Trans. on Image Processing, 2018, 27(2): 568–579. [doi: 10.1109/TIP.2017.2763819]
[43] Feng D, Barnes N, You SD, McCarthy C. Local background enclosure for RGB-D salient object detection. In: Proc. of the 2016 IEEE
Conf. on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016. 2343–2350. [doi: 10.1109/CVPR.2016.257]
[44] Ren JQ, Gong XJ, Yu L, Zhou WH, Yang MY. Exploiting global priors for RGB-D saliency detection. In: Proc. of the 2015 IEEE Conf.
on Computer Vision and Pattern Recognition workshops (CVPR). Boston: IEEE, 2015: 25–32. [doi: 10.1109/CVPRW.2015.7301391]
[45] Chen Q, Liu Z, Zhang Y, Fu KR, Zhao QJ, Du HW. RGB-D salient object detection via 3D convolutional neural networks. In: Proc. of
the 35th AAAI Conf. on Artificial Intelligence (AAAI). AAAI, 2021. 1063–1071. [doi: 10.1609/aaai.v35i2.16191]
[46] Chen H, Li YF. Progressively complementarity-aware fusion network for RGB-D salient object detection. In: Proc. of the 2018
IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018. 3051–3060. [doi: 10.1109/CVPR.
2018.00322]
[47] Fu KR, Fan DP, Ji GP, Zhao QJ, Shen JB, Zhu C. Siamese network for RGB-D salient object detection and beyond. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 2022, 44(9): 5541–5559. [doi: 10.1109/TPAMI.2021.3073689]
[48] Pang YW, Zhang LH, Zhao XQ, Lu HC. Hierarchical dynamic filtering network for RGB-D salient object detection. In: Proc. of the 16th
European Conf. on Computer Vision (ECCV). Glasgow: Springer, 2020. 235–252. [doi: 10.1007/978-3-030-58595-2_15]
[49] Chen ZY, Cong RM, Xu QQ, Huang QM. DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection.
IEEE Trans. on Image Processing, 2021, 30: 7012–7024. [doi: 10.1109/TIP.2020.3028289]
[50] Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-end object detection with Transformers. In: Proc. of the
16th European Conf. on Computer Vision (ECCV). Glasgow: Springer, 2020. 213–229. [doi: 10.1007/978-3-030-58452-8_13]
[51] Zhu XZ, Su WJ, Lu LW, Li B, Wang XG, Dai JF. Deformable DETR: Deformable Transformers for end-to-end object detection. In: Proc.