Manmohan Chandraker

9.9k total citations · 4 hit papers
99 papers, 4.9k citations indexed

About

Manmohan Chandraker is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Artificial Intelligence. According to data from OpenAlex, Manmohan Chandraker has authored 99 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Computer Vision and Pattern Recognition, 23 papers in Computer Graphics and Computer-Aided Design and 22 papers in Artificial Intelligence. Recurrent topics in Manmohan Chandraker's work include Advanced Vision and Imaging (48 papers), Computer Graphics and Visualization Techniques (23 papers) and Advanced Neural Network Applications (20 papers). Manmohan Chandraker is often cited by papers focused on Advanced Vision and Imaging (48 papers), Computer Graphics and Visualization Techniques (23 papers) and Advanced Neural Network Applications (20 papers). Manmohan Chandraker collaborates with scholars based in United States, Japan and Australia. Manmohan Chandraker's co-authors include Yu Xiang, Wongun Choi, Ravi Ramamoorthi, Kihyuk Sohn, Christopher Choy, Zhengqin Li, Philip H. S. Torr, Paul Vernaza, Namhoon Lee and David Kriegman and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and International Journal of Computer Vision.

In The Last Decade

Manmohan Chandraker

95 papers receiving 4.7k citations

Hit Papers

DESIRE: Distant Future Prediction in Dynamic Scenes with ... 2017 2026 2020 2023 2017 2017 2017 2018 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manmohan Chandraker United States 35 3.8k 1.1k 656 654 621 99 4.9k
Lizhuang Ma China 38 3.5k 0.9× 941 0.9× 350 0.5× 156 0.2× 244 0.4× 321 4.9k
Kui Jia China 41 5.8k 1.5× 1.8k 1.6× 419 0.6× 142 0.2× 527 0.8× 119 7.2k
Qifeng Chen Hong Kong 35 4.2k 1.1× 490 0.5× 452 0.7× 168 0.3× 382 0.6× 115 5.2k
Hongbin Zha China 33 2.9k 0.8× 685 0.6× 115 0.2× 452 0.7× 950 1.5× 289 3.9k
Mathieu Salzmann Switzerland 41 4.3k 1.1× 2.0k 1.9× 225 0.3× 100 0.2× 665 1.1× 169 5.8k
Jia‐Bin Huang United States 39 8.8k 2.3× 780 0.7× 693 1.1× 78 0.1× 716 1.2× 117 9.6k
Yongming Rao China 29 2.4k 0.6× 767 0.7× 346 0.5× 139 0.2× 229 0.4× 49 3.4k
Song–Hai Zhang China 23 2.4k 0.6× 528 0.5× 321 0.5× 145 0.2× 240 0.4× 103 3.7k
Errui Ding China 37 4.3k 1.1× 1.2k 1.1× 225 0.3× 135 0.2× 331 0.5× 114 4.9k
Philip Lenz Germany 4 4.4k 1.2× 699 0.7× 168 0.3× 1.1k 1.6× 2.3k 3.7× 5 5.9k

Countries citing papers authored by Manmohan Chandraker

Since Specialization
Citations

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

Fields of papers citing papers by Manmohan Chandraker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manmohan Chandraker

This figure shows the co-authorship network connecting the top 25 collaborators of Manmohan Chandraker. A scholar is included among the top collaborators of Manmohan Chandraker 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 Manmohan Chandraker. Manmohan Chandraker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kalluri, Tarun, Wei‐Yao Wang, Heng Wang, et al.. (2024). Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision. 2693–2703.
2.
Su, Jong-Chyi, et al.. (2024). AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving. 14695–14706. 6 indexed citations
4.
Kalluri, Tarun, Jihyeon Lee, Kihyuk Sohn, et al.. (2024). Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data. 7449–7459. 1 indexed citations
5.
Kalluri, Tarun, Deepak Pathak, Manmohan Chandraker, & Du Tran. (2023). FLAVR: flow-free architecture for fast video frame interpolation. Machine Vision and Applications. 34(5). 1 indexed citations
6.
Stengel, Michael, Eric R. Chan, Zhiding Yu, et al.. (2023). Real-Time Radiance Fields for Single-Image Portrait View Synthesis. ACM Transactions on Graphics. 42(4). 1–15. 32 indexed citations
7.
Kalluri, Tarun, Deepak Pathak, Manmohan Chandraker, & Du Tran. (2023). FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2070–2081. 42 indexed citations
8.
Liu, Chang, Yu Xiang, Yi‐Hsuan Tsai, et al.. (2022). Learning to Learn across Diverse Data Biases in Deep Face Recognition. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4062–4072. 10 indexed citations
9.
Shi, Yichun, Yu Xiang, Kihyuk Sohn, Manmohan Chandraker, & Anil K. Jain. (2020). Towards Universal Representation Learning for Deep Face Recognition. 6816–6825. 103 indexed citations
10.
Wang, Ziyan, Buyu Liu, Samuel Schulter, & Manmohan Chandraker. (2019). A Parametric Top-View Representation of Complex Road Scenes. 10317–10325. 24 indexed citations
11.
Sohn, Kihyuk, Wenling Shang, Yu Xiang, & Manmohan Chandraker. (2018). Unsupervised Domain Adaptation for Distance Metric Learning. International Conference on Learning Representations. 22 indexed citations
12.
Gwak, JunYoung, Christopher Choy, Animesh Garg, Manmohan Chandraker, & Silvio Savarese. (2017). Weakly Supervised Generative Adversarial Networks for 3D Reconstruction.. arXiv (Cornell University). 10 indexed citations
13.
Yin, Xi, Yu Xiang, Kihyuk Sohn, Xiaoming Liu, & Manmohan Chandraker. (2017). Towards Large-Pose Face Frontalization in the Wild. 4010–4019. 215 indexed citations
14.
Li, Chi, M. Zeeshan Zia, Quoc-Huy Tran, et al.. (2017). Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing. 388–397. 56 indexed citations
15.
Lee, Namhoon, Wongun Choi, Paul Vernaza, et al.. (2017). DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 2165–2174. 624 indexed citations breakdown →
16.
Peng, Xi, Yu Xiang, Kihyuk Sohn, Dimitris Metaxas, & Manmohan Chandraker. (2017). Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition. 1632–1641. 102 indexed citations
17.
Choi, Wongun, et al.. (2016). Atomic scenes for scalable traffic scene recognition in monocular videos. 1–9. 5 indexed citations
18.
Chandraker, Manmohan, D. Sudheer Reddy, Yizhou Wang, & Ravi Ramamoorthi. (2013). What Object Motion Reveals about Shape with Unknown BRDF and Lighting. 2523–2530. 10 indexed citations
19.
Chandraker, Manmohan, Jiamin Bai, & Ravi Ramamoorthi. (2012). On Differential Photometric Reconstruction for Unknown, Isotropic BRDFs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(12). 2941–2955. 40 indexed citations
20.
Chandraker, Manmohan & Ravi Ramamoorthi. (2011). What an image reveals about material reflectance. 1076–1083. 31 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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