GCENet: Global contextual exploration network for RGB-D salient object detection

Wang, 2020, Visual saliency guided complex image retrieval, Pattern Recognit. Lett., 130, 64, 10.1016/j.patrec.2018.08.010 Zhang, 2017, Image retrieval using the extended salient region, Inform. Sci., 399, 154, 10.1016/j.ins.2017.03.005 Gao, 2021, Visual tracking via dynamic saliency discriminative correlation filter, Appl. Intell., 1 Liu, 2022, An efficient real-time target tracking algorithm using adaptive feature fusion, J. Vis. Commun. Image Represent., 85, 10.1016/j.jvcir.2022.103505 Y. Zeng, Y. Zhuge, H. Lu, L. Zhang, Joint learning of saliency detection and weakly supervised semantic segmentation, in: Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 7223–7233. Bao, 2021, Visible and thermal images fusion architecture for few-shot semantic segmentation, J. Vis. Commun. Image Represent., 80, 10.1016/j.jvcir.2021.103306 X. Chen, C. Fu, Y. Zhao, F. Zheng, J. Song, R. Ji, Y. Yang, Salience-guided cascaded suppression network for person re-identification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 3300–3310. Zhang, 2021, Global and local information aggregation network for edge-aware salient object detection, J. Vis. Commun. Image Represent., 81, 10.1016/j.jvcir.2021.103350 Wu, 2021, Decomposition and completion network for salient object detection, IEEE Trans. Image Process., 30, 6226, 10.1109/TIP.2021.3093380 Wu, 2021, Decomposition and completion network for salient object detection, IEEE Trans. Image Process., 30, 6226, 10.1109/TIP.2021.3093380 Wu, 2021, Salient object detection via a boundary-guided graph structure, J. Vis. Commun. Image Represent., 75, 10.1016/j.jvcir.2021.103048 J.-X. Zhao, Y. Cao, D.-P. Fan, M.-M. Cheng, X.-Y. Li, L. Zhang, Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3922–3931. Zhang, 2021, Uncertainty inspired RGB-D saliency detection, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2021.3073564 Li, 2021, Hierarchical alternate interaction network for RGB-D salient object detection, IEEE Trans. Image Process., 30, 3528, 10.1109/TIP.2021.3062689 Li, 2020, ICNet: Information conversion network for RGB-D based salient object detection, IEEE Trans. Image Process., 29, 4873, 10.1109/TIP.2020.2976689 T. Zhou, H. Fu, G. Chen, Y. Zhou, D.-P. Fan, L. Shao, Specificity-preserving RGB-D saliency detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2021, pp. 4681–4691. C. Li, R. Cong, Y. Piao, Q. Xu, C.C. Loy, RGB-D salient object detection with cross-modality modulation and selection, in: Proceedings of the European Conference on Computer Vision, 2020, pp. 225–241. Li, 2020, ASIF-net: Attention steered interweave fusion network for RGB-D salient object detection, IEEE Trans. Cybern., 51, 88, 10.1109/TCYB.2020.2969255 Zhang, 2021, Bilateral attention network for RGB-D salient object detection, IEEE Trans. Image Process., 30, 1949, 10.1109/TIP.2021.3049959 W. Zhang, G.-P. Ji, Z. Wang, K. Fu, Q. Zhao, Depth quality-inspired feature manipulation for efficient RGB-D salient object detection, in: Proceedings of the ACM International Conference on Multimedia, 2021, pp. 731–740. G. Li, Z. Liu, L. Ye, Y. Wang, H. Ling, Cross-modal weighting network for RGB-D salient object detection, in: Proceedings of the European Conference on Computer Vision, 2020, pp. 665–681. Ji, 2020, Accurate RGB-D salient object detection via collaborative learning, 52 Jin, 2021, CDNet: Complementary depth network for RGB-D salient object detection, IEEE Trans. Image Process., 30, 3376, 10.1109/TIP.2021.3060167 Z. Liu, Y. Wang, Z. Tu, Y. Xiao, B. Tang, Tritransnet: Rgb-d salient object detection with a triplet transformer embedding network, in: Proceedings of the ACM International Conference on Multimedia, 2021, pp. 4481–4490. Fan, 2020, BBS-net: RGB-D salient object detection with a bifurcated backbone strategy network, 275 K. Fu, D.-P. Fan, G.-P. Ji, Q. Zhao, JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 3049–3059. Z. Wu, L. Su, Q. Huang, Cascaded Partial Decoder for Fast and Accurate Salient Object Detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3902–3911. Ciptadi, 2013 D. Feng, N. Barnes, S. You, C. McCarthy, Local background enclosure for RGB-D salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2343–2350. J. Ren, X. Gong, L. Yu, W. Zhou, M. Ying Yang, Exploiting global priors for RGB-D saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 25–32. Moradi, 2021, A salient object detection framework using linear quadratic regulator controller, J. Vis. Commun. Image Represent., 79, 10.1016/j.jvcir.2021.103259 Xia, 2020, Exploiting background divergence and foreground compactness for salient object detection, Neurocomputing, 383, 194, 10.1016/j.neucom.2019.09.096 D.-P. Fan, Y. Zhai, A. Borji, J. Yang, L. Shao, BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network, in: Proceedings of the European Conference on Computer Vision, 2020, pp. 275–292. Q. Chen, Z. Liu, Y. Zhang, K. Fu, Q. Zhao, H. Du, RGB-D Salient Object Detection via 3D Convolutional Neural Networks, in: Proceedings of the Association for the Advancement of Artificial Intelligence, 2021, pp. 1063–1071. Qu, 2017, RGBD salient object detection via deep fusion, IEEE Trans. Image Process., 26, 2274, 10.1109/TIP.2017.2682981 M. Zhang, W. Ren, Y. Piao, Z. Rong, H. Lu, Select, supplement and focus for RGB-D saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 3472–3481. Zhou, 2020, Universality of deep convolutional neural networks, Appl. Comput. Harmon. Anal., 48, 787, 10.1016/j.acha.2019.06.004 Z. Laskar, J. Kannala, Context aware query image representation for particular object retrieval, in: Proceedings of the Scandinavian Conference on Image Analysis, 2017, pp. 88–99. Z. Qin, P. Zhang, F. Wu, X. Li, Fcanet: Frequency channel attention networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2021, pp. 783–792. F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, X. Tang, Residual attention network for image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3156–3164. Liu, 2018, Automatic salient object sequence rebuilding for video segment analysis, Sci. China Inf. Sci., 61, 1, 10.1007/s11432-016-9150-x Fan, 2020, Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks, IEEE Trans. Neural Netw. Learn. Syst., 32, 2075, 10.1109/TNNLS.2020.2996406 G. Li, Z. Liu, L. Ye, Y. Wang, H. Ling, Cross-modal weighting network for RGB-D salient object detection, in: Proceedings of the European Conference on Computer Vision, 2020, pp. 665–681. Zhou, 2021, RGB-d salient object detection: A survey, Comput. Vis. Media, 7, 37, 10.1007/s41095-020-0199-z Y. Cheng, H. Fu, X. Wei, J. Xiao, X. Cao, Depth enhanced saliency detection method, in: Proceedings of International Conference on Internet Multimedia Computing and Service, 2014, pp. 23–27. Piao, 2019, Saliency detection via depth-induced cellular automata on light field, IEEE Trans. Image Process., 29, 1879, 10.1109/TIP.2019.2942434 H. Peng, B. Li, W. Xiong, W. Hu, R. Ji, RGBD salient object detection: A benchmark and algorithms, in: Proceedings of the European Conference on Computer Vision, 2014, pp. 92–109. R. Ju, L. Ge, W. Geng, T. Ren, G. Wu, Depth saliency based on anisotropic center-surround difference, in: Proceedings of the IEEE International Conference on Image Processing, 2014, pp. 1115–1119. Y. Niu, Y. Geng, X. Li, F. Liu, Leveraging stereopsis for saliency analysis, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 454–461. Y. Pang, L. Zhang, X. Zhao, H. Lu, Hierarchical dynamic filtering network for RGB-D salient object detection, in: Proceedings of the European Conference on Computer Vision, 2020, pp. 235–252. Feng, 2022, Encoder deep interleaved network with multi-scale aggregation for RGB-D salient object detection, Pattern Recognit., 128, 10.1016/j.patcog.2022.108666 R. Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned salient region detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1597–1604. R. Margolin, L. Zelnik-Manor, A. Tal, How to evaluate foreground maps?, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 248–255. D.-P. Fan, C. Gong, Y. Cao, B. Ren, M.-M. Cheng, A. Borji, Enhanced-alignment Measure for Binary Foreground Map Evaluation, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2018, pp. 698–704. F. Perazzi, P. Krähenbühl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 733–740. K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, in: Proceeding of the International Conference on Learning Representations, 2015. D.P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, in: Proceeding of the International Conference on Learning Representations, 2015. Y. Piao, Z. Rong, M. Zhang, W. Ren, H. Lu, A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 9060–9069. Chen, 2022, CFIDNet: Cascaded feature interaction decoder for RGB-D salient object detection, Neural Comput. Appl., 1 X. Zhao, L. Zhang, Y. Pang, H. Lu, L. Zhang, A single stream network for robust and real-time RGB-D salient object detection, in: Proceedings of the European Conference on Computer Vision, 2020, pp. 646–662. Wang, 2022, Learning discriminative cross-modality features for RGB-D saliency detection, IEEE Trans. Image Process. Chen, 2020, RGBD salient object detection via disentangled cross-modal fusion, IEEE Trans. Image Process., 29, 8407, 10.1109/TIP.2020.3014734 Y. Piao, W. Ji, J. Li, M. Zhang, H. Lu, Depth-induced multi-scale recurrent attention network for saliency detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 7254–7263. Chen, 2021, Depth-quality-aware salient object detection, IEEE Trans. Image Process., 30, 2350, 10.1109/TIP.2021.3052069 Wang, 2020, Data-level recombination and lightweight fusion scheme for RGB-D salient object detection, IEEE Trans. Image Process., 30, 458, 10.1109/TIP.2020.3037470 Huang, 2021, Employing bilinear fusion and saliency prior information for RGB-D salient object detection, IEEE Trans. Multimed. Chen, 2019, Three-stream attention-aware network for RGB-D salient object detection, IEEE Trans. Image Process., 28, 2825, 10.1109/TIP.2019.2891104 Zhou, 2021, CCAFNet: Crossflow and cross-scale adaptive fusion network for detecting salient objects in RGB-D images, IEEE Trans. Multimed. P. Sun, W. Zhang, H. Wang, S. Li, X. Li, Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021, pp. 1407–1417.