Adam Kortylewski

2.1k total citations · 1 hit paper
32 papers, 869 citations indexed

About

Adam Kortylewski is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Adam Kortylewski has authored 32 papers receiving a total of 869 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 7 papers in Computational Mechanics. Recurrent topics in Adam Kortylewski's work include Human Pose and Action Recognition (9 papers), Advanced Neural Network Applications (8 papers) and Domain Adaptation and Few-Shot Learning (8 papers). Adam Kortylewski is often cited by papers focused on Human Pose and Action Recognition (9 papers), Advanced Neural Network Applications (8 papers) and Domain Adaptation and Few-Shot Learning (8 papers). Adam Kortylewski collaborates with scholars based in United States, Germany and China. Adam Kortylewski's co-authors include Alan Yuille, Ju He, Yutong Bai, Changhu Wang, Shuai Liu, Jie-Neng Chen, Cheng Yang, Thomas Vetter, Angtian Wang and Bernhard Egger 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

Adam Kortylewski

31 papers receiving 848 citations

Hit Papers

TransFG: A Transformer Architecture for Fine-Grained Reco... 2022 2026 2023 2024 2022 50 100 150 200 250
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.

  1. TransFG: A Transformer Architecture for Fine-Grained Recognition
    2022 270 citations Ju He, Jie-Neng Chen et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Adam Kortylewski
Linjie Yang China
Renrui Zhang China
Yongwei Nie China
Yanbo Fan China
Zhijiang Zhang China
Rohit Girdhar United States
Thanh-Toan Do Australia
Ayan Kumar Bhunia United Kingdom
Stéphane Lathuilière France
Dehui Kong China
Linjie Yang China
Adam Kortylewski
Citations per year, relative to Adam Kortylewski Adam Kortylewski (= 1×) peers Linjie Yang

Countries citing papers authored by Adam Kortylewski

Since Specialization
Citations

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

Fields of papers citing papers by Adam Kortylewski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adam Kortylewski

This figure shows the co-authorship network connecting the top 25 collaborators of Adam Kortylewski. A scholar is included among the top collaborators of Adam Kortylewski 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 Adam Kortylewski. Adam Kortylewski 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.
Zhao, Bingchen, Jiahao Wang, Siwei Yang, et al.. (2024). OOD-CV-v2 : An Extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 11104–11118. 2 indexed citations
2.
Zhang, Yi, et al.. (2024). DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data. 6881–6891. 1 indexed citations
3.
Kortylewski, Adam, et al.. (2024). ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering. 1165–1175. 23 indexed citations
4.
Kortylewski, Adam, et al.. (2024). A Bayesian Approach to OOD Robustness in Image Classification. FreiDok plus (Universitätsbibliothek Freiburg). 17459–17469.
5.
Yang, Jiahao, et al.. (2024). Robust Category-Level 3D Pose Estimation from Diffusion-Enhanced Synthetic Data. FreiDok plus (Universitätsbibliothek Freiburg). 3434–3443. 1 indexed citations
6.
Luvizon, Diogo, Marc Habermann, Vladislav Golyanik, Adam Kortylewski, & Christian Theobalt. (2023). Scene‐Aware 3D Multi‐Human Motion Capture from a Single Camera. Computer Graphics Forum. 42(2). 371–383. 11 indexed citations
7.
Dabral, Rishabh, Adam Kortylewski, Bernhard Egger, et al.. (2023). State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction. Computer Graphics Forum. 42(2). 485–520. 23 indexed citations
8.
Morel-Forster, Andreas, et al.. (2023). Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation. 372–381. 10 indexed citations
9.
Zhan, Fangneng, Yingchen Yu, Rongliang Wu, et al.. (2023). Multimodal Image Synthesis and Editing: The Generative AI Era. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 15098–15119. 61 indexed citations
10.
Pan, Xingang, Mohamed Elgharib, Ayush Tewari, et al.. (2023). AvatarStudio: Text-Driven Editing of 3D Dynamic Human Head Avatars. ACM Transactions on Graphics. 42(6). 1–18. 16 indexed citations
11.
Zhang, Yi, et al.. (2023). 3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation. 9365–9376. 21 indexed citations
12.
He, Ju, Adam Kortylewski, & Alan Yuille. (2023). CORL: Compositional Representation Learning for Few-Shot Classification. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3879–3888. 13 indexed citations
13.
He, Ju, Jie-Neng Chen, Shuai Liu, et al.. (2022). TransFG: A Transformer Architecture for Fine-Grained Recognition. Proceedings of the AAAI Conference on Artificial Intelligence. 36(1). 852–860. 270 indexed citations breakdown →
14.
Gupta, Vipul, et al.. (2022). SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5068–5078. 29 indexed citations
15.
Liu, Qing, et al.. (2022). Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 19118–19129. 18 indexed citations
16.
Kortylewski, Adam, et al.. (2022). Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1205–1214. 16 indexed citations
17.
Kortylewski, Adam, et al.. (2021). Compositional Generative Networks and Robustness to Perceptible Image Changes. 411. 1–8. 1 indexed citations
18.
Wang, Angtian, Adam Kortylewski, & Alan Yuille. (2021). NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation. 1 indexed citations
19.
Kortylewski, Adam, Ju He, Qing Liu, & Alan Yuille. (2020). Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion. 8937–8946. 60 indexed citations
20.
Kortylewski, Adam, Qing Liu, Huiyu Wang, Zhishuai Zhang, & Alan Yuille. (2019). Compositional Convolutional Networks For Robust Object Classification under Occlusion.. arXiv (Cornell University). 2 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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