Chong Mou

1.5k total citations · 2 hit papers
18 papers, 676 citations indexed

About

Chong Mou is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computational Mechanics. According to data from OpenAlex, Chong Mou has authored 18 papers receiving a total of 676 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 4 papers in Media Technology and 3 papers in Computational Mechanics. Recurrent topics in Chong Mou's work include Image and Signal Denoising Methods (6 papers), Advanced Image Processing Techniques (6 papers) and Sparse and Compressive Sensing Techniques (3 papers). Chong Mou is often cited by papers focused on Image and Signal Denoising Methods (6 papers), Advanced Image Processing Techniques (6 papers) and Sparse and Compressive Sensing Techniques (3 papers). Chong Mou collaborates with scholars based in China, Hong Kong and Saudi Arabia. Chong Mou's co-authors include Jian Zhang, Qian Wang, Jian Zhang, Ying Shan, Zhongang Qi, Yanze Wu, Liangbin Xie, Xintao Wang, Siwei Ma and Shiqi Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

In The Last Decade

Chong Mou

15 papers receiving 664 citations

Hit Papers

align trajectories

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.

T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models

2024 259 citations Chong Mou, Xintao Wang et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
Deep Generalized Unfolding Networks for Image Restoration

2022 177 citations Chong Mou, Qian Wang et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chong Mou China	9	506	111	89	83	82	18	676
Naejin Kong South Korea	6	574 1.1×	51 0.5×	149 1.7×	50 0.6×	45 0.5×	7	707
Arsenii Ashukha Russia	4	520 1.0×	52 0.5×	59 0.7×	26 0.3×	181 2.2×	6	694
Peter Sand United States	10	654 1.3×	111 1.0×	133 1.5×	32 0.4×	71 0.9×	11	727
Avinash Sharma India	11	210 0.4×	99 0.9×	46 0.5×	40 0.5×	35 0.4×	60	380
Hyeongwoo Kim Germany	8	935 1.8×	384 3.5×	90 1.0×	34 0.4×	57 0.7×	14	1.1k
Harshith Goka United States	2	402 0.8×	45 0.4×	57 0.6×	23 0.3×	45 0.5×	2	506
Yurui Ren China	9	830 1.6×	65 0.6×	224 2.5×	32 0.4×	25 0.3×	12	887
Tianfan Xue Hong Kong	14	551 1.1×	59 0.5×	151 1.7×	25 0.3×	56 0.7×	36	640
Jacob Munkberg Sweden	15	691 1.4×	287 2.6×	99 1.1×	51 0.6×	41 0.5×	39	925

Countries citing papers authored by Chong Mou

Since Specialization

Citations

This map shows the geographic impact of Chong Mou'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 Chong Mou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chong Mou more than expected).

Fields of papers citing papers by Chong Mou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chong Mou. 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 Chong Mou. The network helps show where Chong Mou may publish in the future.

Co-authorship network of co-authors of Chong Mou

This figure shows the co-authorship network connecting the top 25 collaborators of Chong Mou. A scholar is included among the top collaborators of Chong Mou 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 Chong Mou. Chong Mou 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:

18 of 18 papers shown

1.

Mou, Chong, et al.. (2025). DreamO: A Unified Framework for Image Customization. 1–12. 1 indexed citations

2.

Mou, Chong, et al.. (2025). Diffusion-Based Hierarchical Image Steganography. 1–6.

3.

Mou, Chong, Xintao Wang, Liangbin Xie, et al.. (2024). T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models. Proceedings of the AAAI Conference on Artificial Intelligence. 38(5). 4296–4304. 259 indexed citations breakdown →

4.

Mou, Chong, et al.. (2024). DiffEditor: Boosting Accuracy and Flexibility on Diffusion-Based Image Editing. 8488–8497. 9 indexed citations

5.

Cao, Mingdeng, Chong Mou, Ying Shan, et al.. (2024). ReVideo: Remake a Video with Motion and Content Control. 18481–18505.

6.

Wang, Qian, Weiqi Li, Chong Mou, Xinhua Cheng, & Jian Zhang. (2024). 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model. 6913–6923. 9 indexed citations

7.

Mou, Chong, et al.. (2024). Empowering Real-World Image Super-Resolution With Flexible Interactive Modulation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(11). 7317–7330.

8.

Mou, Chong, et al.. (2023). Optimization-Inspired Cross-Attention Transformer for Compressive Sensing. 6174–6184. 41 indexed citations

9.

Mou, Chong, et al.. (2023). Large-Capacity and Flexible Video Steganography via Invertible Neural Network. 22606–22615. 27 indexed citations

10.

Mou, Chong, Qian Wang, & Jian Zhang. (2022). Deep Generalized Unfolding Networks for Image Restoration. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 17378–17389. 177 indexed citations breakdown →

11.

Mou, Chong, et al.. (2022). Robust Invertible Image Steganography. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7865–7874. 85 indexed citations

12.

Mou, Chong & Jian Zhang. (2022). TransCL: Transformer Makes Strong and Flexible Compressive Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(4). 1–16. 23 indexed citations

13.

Liu, Hangfan, Jian Zhang, & Chong Mou. (2021). Image Denoising Based on Correlation Adaptive Sparse Modeling. 2060–2064. 2 indexed citations

14.

Mou, Chong, et al.. (2021). Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4872–4881. 31 indexed citations

15.

Mou, Chong, et al.. (2021). Dynamic Attentive Graph Learning for Image Restoration. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4308–4317. 1 indexed citations

16.

Mou, Chong & Jian Zhang. (2021). Synergic Feature Attention for Image Restoration. 1850–1854. 1 indexed citations

17.

Mou, Chong & Jian Zhang. (2021). Graph Attention Neural Network for Image Restoration. 1–6. 7 indexed citations

18.

Mou, Chong & Xin Zhang. (2020). Attention Based Dual Branches Fingertip Detection Network and Virtual Key System. 2159–2165. 3 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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