Yongri Piao

3.1k total citations · 1 hit paper
29 papers, 1.8k citations indexed

About

Yongri Piao is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Sensory Systems. According to data from OpenAlex, Yongri Piao has authored 29 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 5 papers in Cognitive Neuroscience and 4 papers in Sensory Systems. Recurrent topics in Yongri Piao's work include Visual Attention and Saliency Detection (24 papers), Advanced Image and Video Retrieval Techniques (14 papers) and Image and Video Quality Assessment (8 papers). Yongri Piao is often cited by papers focused on Visual Attention and Saliency Detection (24 papers), Advanced Image and Video Retrieval Techniques (14 papers) and Image and Video Quality Assessment (8 papers). Yongri Piao collaborates with scholars based in China, Canada and South Korea. Yongri Piao's co-authors include Miao Zhang, Huchuan Lu, Wei Ji, Jingjing Li, Shunyu Yao, Li Cheng, Shuang Xu, Qi Bi, Yu Zhang and Yefeng Zheng and has published in prestigious journals such as IEEE Transactions on Image Processing, International Journal of Computer Vision and IEEE Transactions on Cybernetics.

In The Last Decade

Yongri Piao

29 papers receiving 1.7k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection

2019 351 citations Yongri Piao, Wei Ji et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yongri Piao China	20	1.7k	332	321	194	188	29	1.8k
Mengyang Feng China	10	1.6k 1.0×	445 1.3×	188 0.6×	83 0.4×	225 1.2×	13	1.7k
Gongyang Li China	17	1.2k 0.7×	135 0.4×	456 1.4×	270 1.4×	116 0.6×	43	1.4k
Youwei Pang China	7	961 0.6×	137 0.4×	174 0.5×	135 0.7×	107 0.6×	15	1.1k
Huaizu Jiang United States	11	2.0k 1.2×	245 0.7×	216 0.7×	117 0.6×	395 2.1×	27	2.1k
Lin Zheng China	14	954 0.6×	164 0.5×	152 0.5×	121 0.6×	87 0.5×	50	1.2k
Tie Liu China	11	1.6k 0.9×	248 0.7×	165 0.5×	71 0.4×	379 2.0×	27	1.8k
Zhengzheng Tu China	14	870 0.5×	133 0.4×	256 0.8×	201 1.0×	80 0.4×	42	959
Eleonora Vig Germany	13	984 0.6×	159 0.5×	115 0.4×	187 1.0×	112 0.6×	24	1.2k
Jinqing Qi China	12	891 0.5×	178 0.5×	120 0.4×	70 0.4×	119 0.6×	27	983

Countries citing papers authored by Yongri Piao

Since Specialization

Citations

This map shows the geographic impact of Yongri Piao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yongri Piao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongri Piao more than expected).

Fields of papers citing papers by Yongri Piao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yongri Piao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yongri Piao. The network helps show where Yongri Piao may publish in the future.

Co-authorship network of co-authors of Yongri Piao

This figure shows the co-authorship network connecting the top 25 collaborators of Yongri Piao. A scholar is included among the top collaborators of Yongri Piao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yongri Piao. Yongri Piao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Liu, Tingwei, et al.. (2025). Scene-Aware Background Decoupling via Collaborative Fusion for Video Salient Object Detection. IEEE Signal Processing Letters. 32. 2274–2278. 1 indexed citations

Zhang, Miao, et al.. (2024). CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation. IEEE Transactions on Circuits and Systems for Video Technology. 35(5). 4072–4086. 1 indexed citations

Zhang, Miao, et al.. (2023). Depth Injection Framework for RGBD Salient Object Detection. IEEE Transactions on Image Processing. 32. 5340–5352. 12 indexed citations

Zhang, Miao, et al.. (2022). C$^{2}$DFNet: Criss-Cross Dynamic Filter Network for RGB-D Salient Object Detection. IEEE Transactions on Multimedia. 25. 5142–5154. 68 indexed citations

Ji, Wei, Ge Yan, Jingjing Li, et al.. (2022). DMRA: Depth-Induced Multi-Scale Recurrent Attention Network for RGB-D Saliency Detection. IEEE Transactions on Image Processing. 31. 2321–2336. 45 indexed citations

Piao, Yongri, et al.. (2022). Noise-Sensitive Adversarial Learning for Weakly Supervised Salient Object Detection. IEEE Transactions on Multimedia. 25. 2888–2897. 32 indexed citations

Zhang, Miao, Shuang Xu, Yongri Piao, & Huchuan Lu. (2022). Exploring Spatial Correlation for Light Field Saliency Detection: Expansion From a Single View. IEEE Transactions on Image Processing. 31. 6152–6163. 10 indexed citations

Zhang, Miao, et al.. (2022). PreyNet: Preying on Camouflaged Objects. Proceedings of the 30th ACM International Conference on Multimedia. 5323–5332. 51 indexed citations

Piao, Yongri, et al.. (2021). PANet: Patch-Aware Network for Light Field Salient Object Detection. IEEE Transactions on Cybernetics. 53(1). 379–391. 39 indexed citations

10.

Li, Jingjing, Wei Ji, Qi Bi, et al.. (2021). Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection. Neural Information Processing Systems. 34. 16 indexed citations

11.

Ji, Wei, Jingjing Li, Shuang Yu, et al.. (2021). Calibrated RGB-D Salient Object Detection. 9466–9476. 190 indexed citations

12.

Zhang, Miao, Jie Liu, Yongri Piao, et al.. (2021). Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 1533–1543. 82 indexed citations

13.

Zhang, Miao, et al.. (2020). Select, Supplement and Focus for RGB-D Saliency Detection. 3469–3478. 163 indexed citations

14.

Zhang, Miao, Wei Ji, Yongri Piao, et al.. (2020). LFNet: Light Field Fusion Network for Salient Object Detection. IEEE Transactions on Image Processing. 29. 6276–6287. 79 indexed citations

15.

Zhang, Miao, et al.. (2020). Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection. 4107–4115. 38 indexed citations

16.

Piao, Yongri, Xiaoli Li, Miao Zhang, Jingyi Yu, & Huchuan Lu. (2019). Saliency Detection via Depth-Induced Cellular Automata on Light Field. IEEE Transactions on Image Processing. 29. 1879–1889. 44 indexed citations

17.

Zhang, Miao, Jingjing Li, Wei Ji, Yongri Piao, & Huchuan Lu. (2019). Memory-oriented Decoder for Light Field Salient Object Detection. Neural Information Processing Systems. 32. 896–906. 44 indexed citations

18.

Wang, Tiantian, Yongri Piao, Huchuan Lu, Xiao Li, & Lihe Zhang. (2019). Deep Learning for Light Field Saliency Detection. 8837–8847. 75 indexed citations

19.

Piao, Yongri, et al.. (2009). Robust and Secure InIm-based 3D Watermarking Scheme using Cellular Automata Transform. The Journal of the Korean Institute of Information and Communication Engineering. 13(9). 1767–1778. 4 indexed citations

20.

Piao, Yongri, et al.. (2006). Multi-quantized Image Compression using Wavelet Transform. The Journal of the Korean Institute of Information and Communication Engineering. 10(3). 453–458. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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