liuzywen Goto Github PK

I am a professor in School of Computer Science and Technology, Anhui University, China. My research interests include computer vision and deep learning.

1. Liu Z, Tan Y, He Q, et al. SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(7): 4486-4497. (中科院大类一区Top期刊) （2022.07出版）[高被引论文] [Code]
2. Liu, Zhengyi, Yuan Wang. "TriTransNet:RGB-D salient object detection with a triplet transformer embedding network." ACM MM(2021) (CCF A类会议) [Code]
3. Liu Z, He Q, Wang L, et al. LFTransNet: Light Field Salient Object Detection via a Learnable Weight Descriptor[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(12):7764-7773. (中科院大类一区Top期刊) 2023.12出版[Code]
4. Bin Tang, Liu Z, et al. HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(2): 728-742. (中科院大类一区Top期刊) 2023.02出版[Code]
5. Liu Z, Huang X, Zhang G, et al. Scribble-Supervised RGB-T Salient Object Detection [C]// IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2023: 2369-2374. (CCF B类会议)[Code]
6. Liu Z, Wei Wu, et al. RGB-T Multi-Modal Crowd Counting Based on Transformer. British Machine Vision Conference(BMVC)2022(CCF C类会议) 英国伦敦, 2022年11月21日-24日[Code]
7. Liu Z, Chang B, Cheng F. An interactive filter-wrapper multi-objective evolutionary algorithm for feature selection[J]. Swarm and Evolutionary Computation, 2021: 100925. (中科院大类一区Top期刊)[Code]
8. Liu Z, Zhang Z, Tan Y, et al. Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer[C]//2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022: 140-146. (CCF C类会议) Montreal, QC, Canada加拿大蒙特利尔(在线), 2022年8月21日-25日[Code]
9. Liu Z, Tan Y, Wu W, et al. Dilated high-resolution network driven RGB-T multi-modal crowd counting[J]. Signal Processing: Image Communication, 2023,112: 116915. (中科院大类二区)
10. Liu Z, Bin Zhu, et al. A Simple and Effective Method for RGB-T Salient Object Detection. IEEE International Conference on Ubiquitous Intelligence and Computing(UIC2022). IEEE,2022: 1266-1271 (CCF C类会议) **海口, 2022年12月15日-18日
11. Liu Z, Dong H, Global-Guided Cross-Reference Network for Co-Salient Object Detection. Machine Vision and Applications, 2022, 33(5): 1-13. (中科院大类四区)
12. Liu Z, Wang Y, et al. AGRFNet: Two-stage Cross-modal and Multi-level Attention Gated Recurrent Fusion Network for RGB-D Saliency Detection. Signal Processing: Image Communication, 2022, 104: 116674 (中科院大类二区)
13. Liu Z, Wang Y, et al. BGRDNet: RGB-D Salient Object Detection with a Bidirectional Gated Recurrent Decoding Network[J]. Multimedia Tools and Applications, 2022, 81: 25519–25539. (中科院大类四区)
14. Liu, Zhengyi, Kaixun Wang. "A cross-modal edge-guided salient object detection for RGB-D image." Neurocomputing 454 (2021): 168-177.[paper][password：idso] [code]
15. Liu, Zhengyi, Quanlong Li, and Wei Li. "Deep layer guided network for salient object detection." Neurocomputing 372 (2020): 55-63.[paper][password：agsg] [code]
16. Liu, Zhengyi, Wei Zhang, and Peng Zhao. "A Cross-modal Adaptive Gated Fusion Generative Adversarial Network for RGB-D Salient Object Detection." Neurocomputing 387 (2020): 210-220.[paper][password：bj3u] [Code]
17. Liu, Zhengyi, Song Shi, et al. "Salient object detection for RGB-D image by single stream recurrent convolution neural network." Neurocomputing 363 (2019): 46-57. [paper] [password：kv99][Results]
18. Zhengyi Liu, Tengfei Song, and Feng Xie. "RGB-D image saliency detection from 3D perspective." Multimedia Tools and Applications 78.6 (2019): 6787-6804.[paper][password：dufx]
19. Zhengyi Liu, Jiting Tang. "Salient object detection for RGB-D images by generative adversarial network." Multimedia Tools and Applications 79.35 (2020): 25403-25425.[paper][password:850n] [code]
20. Zhengyi Liu, Jiting Tang, and Peng Zhao. "Salient object detection via hybrid upsampling and hybrid loss computing." The Visual Computer 36 (2020): 843–853.[paper][password:iesu]
21. Liu, Zhengyi, et al. "Robust salient object detection for RGB images." The Visual Computer (2019): 1-13.[paper][password:71o9]
22. Liu, Zhengyi, et al. "Multi-level progressive parallel attention guided salient object detection for RGB-D images." The Visual Computer (2020): 1-12.[paper][password:8woe][Results]
23. Liu, Zhengyi, and Feng Xie. "Co-saliency Detection for RGBD Images Based on Multi-constraint Superpixels Matching and Co-cellular Automata." Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, Cham, 2018.[paper][password:2dc2]
24. 刘政怡,徐天泽. "基于优化的极限学习机和深度层次的 RGB-D 显著检测." 电 子 与 信 息 学 报 41.9 (2019): 2224-2230.
25. 刘政怡,段群涛,石松,赵鹏. "基于多模态特征融合监督的 RGB-D 图像显著性检测." 电子与信息学报 42.4 (2019): 997-1004.
26. 李炜,李全龙,刘政怡. "基于加权的 K 近邻线性混合显著性目标检测." 电子与信息学报 41.10 (2019): 2442-2449.
27. 刘政怡,黄子超,张志华. “显著中心先验和显著-深度概率矫正的RGB-D显著目标检测” 电子与信息学报 39.12 (2017): 2945-2952.
28. 吴建国,邵婷,刘政怡."融合显著深度特征的 RGB-D 图像显著目标检测." 电子与信息学报 39.9 (2017): 2148-2154.
29. Liu, Zhengyi, et al. "Salient object detection for RGB-D images by generative adversarial network." Multimedia Tools and Applications (2020): 1-23.[paper][password:nn7j][Results]
30. Adaptive-Selection-Training-Model-for-Salient-Object-Detection[underview][Results][password：1mfv]

1. 一种RGB-D图像显著性计算方法，国家发明专利，2020年1月，（ZL2017101418884），第一发明人。
2. 一种基于PSO的RGBD图的协同显著目标检测方法，国家发明专利，2021年7月，（ZL2018114033704），第一发明人。
3. 一种基于单流深度网络的RGB-D显著目标检测方法，国家发明专利，2021年8月，（ZL2018114034020），第一发明人。
4. 一种基于注意力机制的图像协同显著目标检测模型，国家发明专利，2022年11月，（ZL2020101092400），第一发明人。
5. 一种具有自适应选择训练过程的图像显著目标检测方法，国家发明专利，2023年4月，（ZL201911261553.1），第一发明人。
6. 半监督 RGB-D 图像镜面检测方法、存储介质及计算机设备，国家发明专利，2024年1月，（ZL202311498290.2），第一发明人。