skip to main content
research-article

Rethinking Feature Mining for Light Field Salient Object Detection

Published: 29 October 2024 Publication History

Abstract

Light field salient object detection (LF SOD) has recently received increasing attention. However, most current works typically rely on an individual focal stack backbone for feature extraction. This manner ignores the characteristic of blurred saliency-related regions and contour within focal slices, resulting in insufficient or even inaccurate saliency responses. Aiming at addressing this issue, we rethink the feature mining (i.e., exploration) within focal slices and focus on exploiting informative focal slice features and fully leveraging contour information for accurate LF SOD. First, we observe that the geometric relation between different regions within the focal slices is conducive to useful saliency feature mining if utilized properly. In light of this, we propose an implicit graph learning (IGL) approach. The IGL constructs graph structures to propagate informative geometric relations within the focal slices and all-focus features, and promotes crucial and discriminative focal stack feature mining via graph feature distillation. Second, unlike previous works that rarely utilize contour information, we propose a reciprocal refinement fusion (RRF) strategy. This strategy encourages saliency features and object contour cues to effectively complement each other. Furthermore, a contour hint injection mechanism is introduced to refine the feature expressions. Extensive experiments showcase the superiority of our approach over previous state-of-the-art models with an efficient real-time inference speed. Codes are available at https://github.com/gbliao/IRNet and https://openi.pcl.ac.cn/OpenVision/IRNet.

References

[1]
Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk. 2009. Frequency-tuned salient region detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1597–1604.
[2]
Baian Chen, Zhilei Chen, Xiaowei Hu, Jun Xu, Haoran Xie, Jing Qin, and Mingqiang Wei. 2023. Dynamic message propagation network for RGB-D and video salient object detection. ACM Transactions on Multimedia Computing, Communications and Applications 20, 1 (2023), 1–21.
[3]
Geng Chen, Huazhu Fu, Tao Zhou, Guobao Xiao, Keren Fu, Yong Xia, and Yanning Zhang. 2023. Fusion-embedding Siamese network for light field salient object detection. IEEE Transactions on Multimedia 26 (2023), 984–994.
[4]
Qian Chen, Ze Liu, Yi Zhang, Keren Fu, Qijun Zhao, and Hongwei Du. 2021. RGB-D salient object detection via 3D convolutional neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 1063–1071.
[5]
Yilei Chen, Gongyang Li, Ping An, Zhi Liu, Xinpeng Huang, and Qiang Wu. 2024. Light field salient object detection with sparse views via complementary and discriminative interaction network. IEEE Transactions on Circuits and Systems for Video Technology 34, 2 (2024), 1070–1085.
[6]
Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, and Shi-Min Hu. 2014. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 3 (2014), 569–582.
[7]
Yu Ding, Zhi Liu, Mengke Huang, Ran Shi, and Xiangyang Wang. 2019. Depth-aware saliency detection using convolutional neural networks. Journal of Visual Communication and Image Representation 61 (2019), 1–9.
[8]
Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. 2017. Structure-measure: A new way to evaluate foreground maps. In Proceedings of the IEEE International Conference on Computer Vision. 4548–4557.
[9]
Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. 2018. Enhanced-alignment measure for binary foreground map evaluation. In Proceedings of the International Joint Conference on Artificial Intelligence. 698–704.
[10]
Deng-Ping Fan, Zheng Lin, Zhao Zhang, Menglong Zhu, and Ming-Ming Cheng. 2020. Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks. IEEE Transactions on Neural Networks and Learning Systems 32, 5 (2020), 2075–2089.
[11]
Songlin Fan, Wei Gao, and Ge Li. 2022a. Salient object detection for point clouds. In Proceedings of the European Conference on Computer Vision. Springer, 1–19.
[12]
Xiaoqing Fan, Ge Li, Dingquan Li, Yurui Ren, Wei Gao, and Thomas H. Li. 2022. Deep geometry post-processing for decompressed point clouds. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME ’22). IEEE, 1–6.
[13]
David Feng, Nick Barnes, Shaodi You, and Chris McCarthy. 2016. Local background enclosure for RGB-D salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2343–2350.
[14]
Keren Fu, Deng-Ping Fan, Ge-Peng Ji, and Qijun Zhao. 2020. 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. 3052–3062.
[15]
Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, and Ce Zhu. 2021. Siamese network for RGB-D salient object detection and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2021), 5541–5559.
[16]
Keren Fu, Yao Jiang, Ge-Peng Ji, Tao Zhou, Qijun Zhao, and Deng-Ping Fan. 2022. Light field salient object detection: A review and benchmark. Computational Visual Media 8, 4 (2022), 509–534.
[17]
Wei Gao, Songlin Fan, Ge Li, and Weisi Lin. 2023. A thorough benchmark and a new model for light field saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 7 (2023), 8003–8019.
[18]
Wei Gao, Qiuping Jiang, Ronggang Wang, Siwei Ma, Ge Li, and Sam Kwong. 2021a. Consistent quality oriented rate control in HEVC via balancing intra and inter frame coding. IEEE Transactions on Industrial Informatics 18, 3 (2021), 1594–1604.
[19]
Wei Gao, Guibiao Liao, Siwei Ma, Ge Li, Yongsheng Liang, and Weisi Lin. 2022. Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection. IEEE Transactions on Circuits and Systems for Video Technology 32, 4 (2022), 2091–2106.
[20]
Wei Gao, Shangkun Sun, Huiming Zheng, Yuyang Wu, Hua Ye, and Yongchi Zhang. 2023b. OpenDMC: An open-source library and performance evaluation for deep-learning-based multi-frame compression. In Proceedings of the 31st ACM International Conference on Multimedia. 9685–9688.
[21]
Wei Gao, Lvfang Tao, Linjie Zhou, Dinghao Yang, Xiaoyu Zhang, and Zixuan Guo. 2020. Low-rate image compression with super-resolution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 154–155.
[22]
Wei Gao, Hua Ye, Ge Li, Huiming Zheng, Yuyang Wu, and Liang Xie. 2022. OpenPointCloud: An open-source algorithm library of deep learning based point cloud compression. In Proceedings of the 30th ACM International Conference on Multimedia. 7347–7350.
[23]
Wei Gao, Hang Yuan, Yang Guo, Lvfang Tao, Zhanyuan Cai, and Ge Li. 2022. OpenHardwareVC: An open source library for 8k UHD video coding hardware implementation. In Proceedings of the 30th ACM International Conference on Multimedia. 7339–7342.
[24]
Wei Gao, Linjie Zhou, and Lvfang Tao. 2021. A fast view synthesis implementation method for light field applications. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17, 4 (2021), 1–20.
[25]
Yuan Gao, Miaojing Shi, Dacheng Tao, and Chao Xu. 2015. Database saliency for fast image retrieval. IEEE Transactions on Multimedia 17, 3 (2015), 359–369.
[26]
Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005. A new model for learning in graph domains. In Proceedings of the IEEE International Joint Conference on Neural Networks 2005, Vol. 2. IEEE, 729–734.
[27]
Yang Guo, Wei Gao, Siwei Ma, and Ge Li. 2022. Accelerating transform algorithm implementation for efficient intra coding of 8k UHD videos. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 18, 4 (2022), 1–20.
[28]
Zixuan Guo, Wei Gao, Haiqiang Wang, Junle Wang, and Songlin Fan. 2021. No-reference deep quality assessment of compressed light field images. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME ’21). IEEE, 1–6.
[29]
Junwei Han, Hao Chen, Nian Liu, Chenggang Yan, and Xuelong Li. 2017. CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion. IEEE Transactions on Cybernetics 48, 11 (2017), 3171–3183.
[30]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.
[31]
Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7132–7141.
[32]
Shuaixiong Hui, Qiang Guo, Xiaoyu Geng, and Caiming Zhang. 2023. Multi-guidance CNNs for salient object detection. ACM Transactions on Multimedia Computing, Communications and Applications 19, 3 (2023), 1–19.
[33]
Yao Jiang, Wenbo Zhang, Keren Fu, and Qijun Zhao. 2022. MEANet: Multi-modal edge-aware network for light field salient object detection. Neurocomputing 491 (2022), 78–90.
[34]
Dong Jing, Shuo Zhang, Runmin Cong, and Youfang Lin. 2021. Occlusion-aware bi-directional guided network for light field salient object detection. In Proceedings of the ACM International Conference on Multimedia. 1692–1701.
[35]
Gongyang Li, Zhi Liu, Minyu Chen, Zhen Bai, Weisi Lin, and Haibin Ling. 2021. Hierarchical alternate interaction network for RGB-D salient object detection. IEEE Transactions on Image Processing 30 (2021), 3528–3542.
[36]
Nianyi Li, Bilin Sun, and Jingyi Yu. 2015. A weighted sparse coding framework for saliency detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5216–5223.
[37]
Nianyi Li, Jinwei Ye, Yu Ji, Haibin Ling, and Jingyi Yu. 2014. Saliency detection on light field. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2806–2813.
[38]
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv:1511.05493. Retrieved from
[39]
Zijian Liang, Pengjie Wang, Ke Xu, Pingping Zhang, and Rynson WH Lau. 2022. Weakly-supervised salient object detection on light fields. IEEE Transactions on Image Processing 31 (2022), 6295–6305.
[40]
Guibiao Liao, Wei Gao, Qiuping Jiang, Ronggang Wang, and Ge Li. 2020. MMNet: Multi-stage and multi-scale fusion network for RGB-D salient object detection. In Proceedings of the ACM International Conference on Multimedia. 2436–2444.
[41]
Guibiao Liao, Wei Gao, Ge Li, Junle Wang, and Sam Kwong. 2022. Cross-collaborative fusion-encoder network for robust RGB-thermal salient object detection. IEEE Transactions on Circuits and Systems for Video Technology 32, 11 (2022), 7646–7661.
[42]
Guibiao Liao, Jiankun Li, and Xiaoqing Ye. 2024a. VLM2Scene: Self-supervised image-text-LiDAR Learning with foundation models for autonomous driving scene understanding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 3351–3359.
[43]
Guibiao Liao, Kaichen Zhou, Zhenyu Bao, Kanglin Liu, and Qing Li. 2024b. OV-NeRF: Open-vocabulary neural radiance fields with vision and language foundation models for 3D semantic understanding. arXiv:2402.04648.
[44]
Feng Lin, Wengang Zhou, Jiajun Deng, Bin Li, Yan Lu, and Houqiang Li. 2021. Residual refinement network with attribute guidance for precise saliency detection. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17, 3 (2021), 1–19.
[45]
Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, and Jianmin Jiang. 2019. A simple pooling-based design for real-time salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3917–3926.
[46]
Nian Liu, Ni Zhang, and Junwei Han. 2020. Learning selective self-mutual attention for RGB-D saliency detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 13756–13765.
[47]
Nian Liu, Wangbo Zhao, Dingwen Zhang, Junwei Han, and Ling Shao. 2021. Light field saliency detection with dual local graph learning and reciprocative guidance. In Proceedings of the IEEE International Conference on Computer Vision. 4712–4721.
[48]
Zhengyi Liu, Qian He, Linbo Wang, Xianong Fang, and Bin Tang. 2023. LFTransNet: Light field salient object detection via a learnable weight descriptor. IEEE Transactions on Circuits and Systems for Video Technology 33, 12 (2023), 7764–7773.
[49]
Ao Luo, Xin Li, Fan Yang, Zhicheng Jiao, Hong Cheng, and Siwei Lyu. 2020. Cascade graph neural networks for RGB-D salient object detection. In Proceedings of the 16th European Conference on Computer Vision (ECCV ’20). 346–364.
[50]
Cong Ma, Zhenjiang Miao, Xiao-Ping Zhang, and Min Li. 2017. A saliency prior context model for real-time object tracking. IEEE Transactions on Multimedia 19, 11 (2017), 2415–2424.
[51]
Yu-Fei Ma, Lie Lu, Hong-Jiang Zhang, and Mingjing Li. 2002. A user attention model for video summarization. In Proceedings of the ACM international conference on Multimedia. 533–542.
[52]
Youwei Pang, Xiaoqi Zhao, Lihe Zhang, and Huchuan Lu. 2020. Multi-scale interactive network for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9413–9422.
[53]
Houwen Peng, Bing Li, Weihua Xiong, Weiming Hu, and Rongrong Ji. 2014. Rgbd salient object detection: A benchmark and algorithms. In Proceedings of the European Conference on Computer Vision. 92–109.
[54]
Federico Perazzi, Philipp Krähenbühl, Yael Pritch, and Alexander Hornung. 2012. Saliency filters: Contrast based filtering for salient region detection. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 733–740.
[55]
Yongri Piao, Yongyao Jiang, Miao Zhang, Jian Wang, and Huchuan Lu. 2021. PANet: Patch-aware network for light field salient object detection. IEEE Transactions on Cybernetics 53, 1 (2021), 379–391.
[56]
Yongri Piao, Zhengkun Rong, Miao Zhang, Xiao Li, and Huchuan Lu. 2019. Deep light-field-driven saliency detection from a single view. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19). 904–911.
[57]
Yongri Piao, Zhengkun Rong, Miao Zhang, and Huchuan Lu. 2020. Exploit and replace: An asymmetrical two-stream architecture for versatile light field saliency detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 11865–11873.
[58]
Franco Scarselli, Marco Gori, Ah C. Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE Transactions on Neural Networks 20, 1 (2008), 61–80.
[59]
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-Chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems 28 (2015), 802–810.
[60]
Fei Song, Ge Li, Wei Gao, and Thomas H. Li. 2022. Rate-distortion optimized graph for point cloud attribute coding. IEEE Signal Processing Letters 29 (2022), 922–926.
[61]
Fei Song, Ge Li, Xiaodong Yang, Wei Gao, and Shan Liu. 2023. Block-adaptive point cloud attribute coding with region-aware optimized transform. IEEE Transactions on Circuits and Systems for Video Technology 33, 8 (2023), 4294–4308.
[62]
Hongmei Song, Wenguan Wang, Sanyuan Zhao, Jianbing Shen, and Kin-Man Lam. 2018. Pyramid dilated deeper convLSTM for video salient object detection. In Proceedings of the European Conference on Computer Vision. 715–731.
[63]
Kechen Song, Liming Huang, Aojun Gong, and Yunhui Yan. 2023. Multiple graph affinity interactive network and a variable illumination dataset for RGBT image salient object detection. IEEE Transactions on Circuits and Systems for Video Technology 33, 7 (2023), 3104–3118.
[64]
Yangfan Sun, Zhu Li, Li Li, Shizheng Wang, and Wei Gao. 2022. Optimization of compressive light field display in dual-guided learning. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP ’22). IEEE, 2075–2079.
[65]
Yangfan Sun, Zhu Li, Shizheng Wang, and Wei Gao. 2023. Depth-assisted calibration on learning-based factorization for a compressive light field display. Optics Express 31, 4 (2023), 5399–5413.
[66]
Nianyi Li, Jinwei Ye, Yu Ji, Haibin Ling, and Jingyi Yu. 2017. Saliency detection on light field. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 8 (2017), 1605–1616.
[67]
Qiudan Zhang, Shiqi Wang, Xu Wang, Zhenhao Sun, Sam Kwong, and Jianmin Jiang. 2021. Geometry auxiliary salient object detection for light fields via graph neural networks. IEEE Transactions on Image Processing 30 (2021), 7578–7592.
[68]
Ruimin Wang, Fasheng Wang, Yiming Su, Jing Sun, Fuming Sun, and Haojie Li. 2023. Attention-guided multi-modality interaction network for RGB-D salient object detection. ACM Transactions on Multimedia Computing, Communications and Applications 20, 3 (2023), 1–22.
[69]
Tiantian Wang, Ali Borji, Lihe Zhang, Pingping Zhang, and Huchuan Lu. 2017. A stagewise refinement model for detecting salient objects in images. In Proceedings of the IEEE International Conference on Computer Vision. 4019–4028.
[70]
Tiantian Wang, Yongri Piao, Xiao Li, Lihe Zhang, and Huchuan Lu. 2019. Deep learning for light field saliency detection. In Proceedings of the IEEE International Conference on Computer Vision. 8838–8848.
[71]
Wenguan Wang, Qiuxia Lai, Huazhu Fu, Jianbing Shen, Haibin Ling, and Ruigang Yang. 2021. Salient object detection in the deep learning era: An in-depth survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 6 (2021), 3239–3259.
[72]
Wenguan Wang, Xiankai Lu, Jianbing Shen, David J. Crandall, and Ling Shao. 2019. Zero-shot video object segmentation via attentive graph neural networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9236–9245.
[73]
Wenguan Wang, Jianbing Shen, Ming-Ming Cheng, and Ling Shao. 2019. An iterative and cooperative top-down and bottom-up inference network for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5968–5977.
[74]
Wenguan Wang, Jianbing Shen, Ruigang Yang, and Fatih Porikli. 2017. Saliency-aware video object segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 1 (2017), 20–33.
[75]
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 568–578.
[76]
Wenguan Wang, Shuyang Zhao, Jianbing Shen, Steven C. H. Hoi, and Ali Borji. 2019. Salient object detection with pyramid attention and salient edges. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1448–1457.
[77]
Xingzheng Wang, Songwei Chen, Guoyao Wei, and Jiehao Liu. 2023. TENet: Accurate light-field salient object detection with a transformer embedding network. Image and Vision Computing 129 (2023), 104595.
[78]
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He. 2018. Non-local neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7794–7803.
[79]
Jun Wei, Shuhui Wang, Zhe Wu, Chi Su, Qingming Huang, and Qi Tian. 2020. Label decoupling framework for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 13025–13034.
[80]
Yuyang Wu, Zhiyang Qi, Huiming Zheng, Lvfang Tao, and Wei Gao. 2021. Deep image compression with latent optimization and piece-wise quantization approximation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1926–1930.
[81]
Zhe Wu, Li Su, and Qingming Huang. 2019. Cascaded partial decoder for fast and accurate salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3907–3916.
[82]
Chenxi Xie, Changqun Xia, Mingcan Ma, Zhirui Zhao, Xiaowu Chen, and Jia Li. 2022. Pyramid grafting network for one-stage high resolution saliency detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11717–11726.
[83]
Zetao Yang, Wei Gao, Ge Li, and Yiqiang Yan. 2023. Sur-driven video coding rate control for jointly optimizing perceptual quality and buffer control. IEEE Transactions on Image Processing (2023).
[84]
Bo Yuan, Yao Jiang, Keren Fu, and Qijun Zhao. 2023. Guided focal stack refinement network for light field salient object detection. In IEEE International Conference on Multimedia and Expo. IEEE, 2387–2392.
[85]
Hang Yuan, Wei Gao, Ge Li, and Zhu Li. 2022. Rate-distortion-guided learning approach with cross-projection information for V-PCC fast CU decision. In Proceedings of the 30th ACM International Conference on Multimedia. 3085–3093.
[86]
Yingjie Zhai, Deng-Ping Fan, Jufeng Yang, Ali Borji, Ling Shao, Junwei Han, and Liang Wang. 2021. Bifurcated backbone strategy for RGB-D salient object detection. IEEE Transactions on Image Processing 30 (2021), 8727–8742.
[87]
Jun Zhang, Yamei Liu, Shengping Zhang, Ronald Poppe, and Meng Wang. 2020. Light field saliency detection with deep convolutional networks. IEEE Transactions on Image Processing 29 (2020), 4421–4434.
[88]
Jun Zhang, Meng Wang, Jun Gao, Yi Wang, Xudong Zhang, and Xindong Wu. 2015. Saliency detection with a deeper investigation of light field. In Proceedings of the International Joint Conference on Artificial Intelligence. 2212–2218.
[89]
Jun Zhang, Meng Wang, Liang Lin, Xun Yang, Jun Gao, and Yong Rui. 2017. Saliency detection on light field: A multi-cue approach. ACM Transactions on Multimedia Computing, Communications, and Applications 13, 3 (2017), 1–22.
[90]
Miao Zhang, Sun Xiao Fei, Jie Liu, Shuang Xu, Yongri Piao, and Huchuan Lu. 2020. Asymmetric two-stream architecture for accurate rgb-d saliency detection. In Proceedings of the European Conference on Computer Vision. 374–390.
[91]
Miao Zhang, Wei Ji, Yongri Piao, Jingjing Li, Yu Zhang, Shuang Xu, and Huchuan Lu. 2020. LFNet: Light field fusion network for salient object detection. IEEE Transactions on Image Processing 29 (2020), 6276–6287.
[92]
Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, and Huchuan Lu. 2019. Memory-oriented decoder for light field salient object detection. Advances in Neural Information Processing Systems 32 (2019).
[93]
Miao Zhang, Weisong Ren, Yongri Piao, Zhengkun Rong, and Huchuan Lu. 2020. Select, supplement and focus for RGB-D saliency detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3472–3481.
[94]
Miao Zhang, Shuang Xu, Yongri Piao, and Huchuan Lu. 2022. Exploring spatial correlation for light field saliency detection: Expansion from a single view. IEEE Transactions on Image Processing 31 (2022), 6152–6163.
[95]
Nan Zhang, Zhiyi Pan, Thomas H. Li, Wei Gao, and Ge Li. 2023. Improving graph representation for point cloud segmentation via attentive filtering. In Proceedings of the IEEE/CVF Conference on Computer Vision And Pattern Recognition. 1244–1254.
[96]
Qiudan Zhang, Shiqi Wang, Xu Wang, Zhenhao Sun, Sam Kwong, and Jianmin Jiang. 2020. A multi-task collaborative network for light field salient object detection. IEEE Transactions on Circuits and Systems for Video Technology 31, 5 (2020), 1849–1861.
[97]
Xiaoyu Zhang, Guibiao Liao, Wei Gao, and Ge Li. 2022. TDRnet: Transformer-based dual-branch restoration network for geometry based point cloud compression artifacts. In Proceedings of the IEEE International Conference on Multimedia and Expo. IEEE, 1–6.
[98]
Yongchi Zhang, Wei Gao, and Ge Li. 2022. OpenPointCloud-V2: A deep learning based open-source algorithm library of point cloud processing. In Proceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis. 51–55.
[99]
Jia-Xing Zhao, Jiang-Jiang Liu, Deng-Ping Fan, Yang Cao, Jufeng Yang, and Ming-Ming Cheng. 2019. EGNet: Edge guidance network for salient object detection. In Proceedings of the IEEE International Conference on Computer Vision. 8779–8788.
[100]
Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. 2020. Suppress and balance: A simple gated network for salient object detection. In Proceedings of the European Conference on Computer Vision. 35–51.
[101]
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1 (2020), 57–81.
[102]
Linjie Zhou, Wei Gao, and Ge Li. 2022. End-to-end spatial-angular light field super-resolution using parallax structure preservation strategy. In Proceedings of the IEEE International Conference on Image Processing (ICIP ’22). IEEE, 3396–3400.
[103]
Linjie Zhou, Wei Gao, Ge Li, Hui Yuan, Tiesong Zhao, and Guanghui Yue. 2023. Disentangled feature distillation for light field super-resolution with degradations. In Proceedings of the IEEE International Conference on Multimedia and Expo Workshops (ICMEW ’23). IEEE, 116–121.
[104]
Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. 2021. RGB-D salient object detection: A survey. Computational Visual Media 7, 1 (2021), 37–69.
[105]
Mingchen Zhuge, Deng-Ping Fan, Nian Liu, Dingwen Zhang, Dong Xu, and Ling Shao. 2022. Salient object detection via integrity learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 3 (2022), 3738–3752.

Cited By

View all
  • (2024)SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor AttacksProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785718:4(1-24)Online publication date: 21-Nov-2024
  • (2024)Light field angular super-resolution by view-specific queriesThe Visual Computer10.1007/s00371-024-03620-yOnline publication date: 22-Sep-2024
  • (2024)Open-Source Projects for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_9(255-272)Online publication date: 10-Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 20, Issue 10
October 2024
729 pages
EISSN:1551-6865
DOI:10.1145/3613707
  • Editor:
  • Abdulmotaleb El Saddik
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2024
Online AM: 08 July 2024
Accepted: 20 June 2024
Revised: 06 May 2024
Received: 30 November 2023
Published in TOMM Volume 20, Issue 10

Check for updates

Author Tags

  1. Light field salient object detection
  2. implicit graph learning
  3. reciprocal refinement fusion
  4. real-time speed

Qualifiers

  • Research-article

Funding Sources

  • The Major Key Project of PCL
  • Natural Science Foundation of China
  • Guangdong Province Pearl River Talent Program
  • Guangdong Basic and Applied Basic Research Foundation
  • Shenzhen Science and Technology Program
  • CAAI-MindSpore Open Fund, developed on OpenI Community

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)299
  • Downloads (Last 6 weeks)54
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor AttacksProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785718:4(1-24)Online publication date: 21-Nov-2024
  • (2024)Light field angular super-resolution by view-specific queriesThe Visual Computer10.1007/s00371-024-03620-yOnline publication date: 22-Sep-2024
  • (2024)Open-Source Projects for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_9(255-272)Online publication date: 10-Oct-2024
  • (2024)Point Cloud-Language Multi-modal LearningDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_8(227-254)Online publication date: 10-Oct-2024
  • (2024)Point Cloud Pre-trained Models and Large ModelsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_7(195-225)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-Based Point Cloud Analysis IIDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_6(163-193)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-Based Point Cloud Analysis IDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_5(131-162)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-Based Point Cloud Enhancement IIDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_4(99-130)Online publication date: 10-Oct-2024
  • (2024)Deep-Learning-based Point Cloud Enhancement IDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_3(71-97)Online publication date: 10-Oct-2024
  • (2024)Learning Basics for 3D Point CloudsDeep Learning for 3D Point Clouds10.1007/978-981-97-9570-3_2(29-70)Online publication date: 10-Oct-2024
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media