Anna Khoreva

2.6k total citations · 2 hit papers
18 papers, 1.3k citations indexed

About

Anna Khoreva is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Anna Khoreva has authored 18 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 3 papers in Media Technology. Recurrent topics in Anna Khoreva's work include Advanced Neural Network Applications (8 papers), Generative Adversarial Networks and Image Synthesis (7 papers) and Visual Attention and Saliency Detection (5 papers). Anna Khoreva is often cited by papers focused on Advanced Neural Network Applications (8 papers), Generative Adversarial Networks and Image Synthesis (7 papers) and Visual Attention and Saliency Detection (5 papers). Anna Khoreva collaborates with scholars based in Germany, India and United States. Anna Khoreva's co-authors include Bernt Schiele, Rodrigo Benenson, Matthias Hein, Jan Hosang, Federico Perazzi, Alexander Sorkine‐Hornung, Eddy Ilg, Thomas Brox, Seong Joon Oh and Zeynep Akata and has published in prestigious journals such as International Journal of Computer Vision, Computer Vision and Image Understanding and UvA-DARE (University of Amsterdam).

In The Last Decade

Anna Khoreva

18 papers receiving 1.2k 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.

Simple Does It: Weakly Supervised Instance and Semantic Segmentation

2017 468 citations Anna Khoreva, Rodrigo Benenson et al. profile →
Learning Video Object Segmentation from Static Images

2017 337 citations Federico Perazzi, Anna Khoreva et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Anna Khoreva Germany	11	1.1k	379	72	59	49	18	1.3k
Pedro O. Pinheiro Canada	9	691 0.6×	366 1.0×	88 1.2×	57 1.0×	32 0.7×	13	968
Chenxi Liu United States	6	693 0.6×	412 1.1×	92 1.3×	81 1.4×	33 0.7×	12	916
Xianxu Hou China	16	646 0.6×	297 0.8×	108 1.5×	102 1.7×	62 1.3×	38	959
Gedas Bertasius United States	14	823 0.7×	261 0.7×	110 1.5×	46 0.8×	36 0.7×	35	1.0k
David Dohan United States	5	700 0.6×	548 1.4×	71 1.0×	132 2.2×	50 1.0×	10	1.0k
Jianbo Jiao United Kingdom	17	780 0.7×	231 0.6×	204 2.8×	94 1.6×	54 1.1×	58	1.0k
Alexandre Sablayrolles France	5	437 0.4×	261 0.7×	102 1.4×	72 1.2×	42 0.9×	7	756
Nam-Gyu Cho South Korea	6	876 0.8×	382 1.0×	70 1.0×	22 0.4×	84 1.7×	8	986

Countries citing papers authored by Anna Khoreva

Since Specialization

Citations

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

Fields of papers citing papers by Anna Khoreva

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anna Khoreva

This figure shows the co-authorship network connecting the top 25 collaborators of Anna Khoreva. A scholar is included among the top collaborators of Anna Khoreva 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 Anna Khoreva. Anna Khoreva 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

Zhang, Dan, et al.. (2023). Generating novel scene compositions from single images and videos. Computer Vision and Image Understanding. 239. 103888–103888. 7 indexed citations

Zhang, Dan, et al.. (2023). Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization. International Journal of Computer Vision. 132(2). 446–465. 4 indexed citations

Zhang, Dan, et al.. (2023). One-Shot Synthesis of Images and Segmentation Masks. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 6274–6283. 5 indexed citations

Borges, Júlio C., et al.. (2023). Discovering Class-Specific GAN Controls for Semantic Image Synthesis. 688–697. 2 indexed citations

Zhang, Dan, et al.. (2023). Intra-Source Style Augmentation for Improved Domain Generalization. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 509–519. 13 indexed citations

Zhang, Dan, et al.. (2022). OASIS: Only Adversarial Supervision for Semantic Image Synthesis. International Journal of Computer Vision. 130(12). 2903–2923. 24 indexed citations

Zhang, Dan & Anna Khoreva. (2019). Progressive Augmentation of GANs. Neural Information Processing Systems. 32. 6249–6259. 5 indexed citations

Hoyer, Lukas, et al.. (2019). Grid Saliency for Context Explanations of Semantic Segmentation. Neural Information Processing Systems. 32. 6459–6470. 6 indexed citations

Khoreva, Anna, Rodrigo Benenson, Eddy Ilg, Thomas Brox, & Bernt Schiele. (2019). Lucid Data Dreaming for Video Object Segmentation. International Journal of Computer Vision. 127(9). 1175–1197. 79 indexed citations

10.

Khoreva, Anna, et al.. (2018). Learning to Refine Human Pose Estimation. 318–31809. 61 indexed citations

11.

Zhang, Dan & Anna Khoreva. (2018). PA-GAN: Improving GAN Training by Progressive Augmentation. 9 indexed citations

12.

Khoreva, Anna, Rodrigo Benenson, Eddy Ilg, Thomas Brox, & Bernt Schiele. (2017). Lucid Data Dreaming for Object Tracking. arXiv (Cornell University). 38 indexed citations

13.

Khoreva, Anna, Rodrigo Benenson, Eddy Ilg, Thomas Brox, & Bernt Schiele. (2017). Lucid Data Dreaming for Multiple Object Tracking. arXiv (Cornell University). 15 indexed citations

14.

Khoreva, Anna, Rodrigo Benenson, Jan Hosang, Matthias Hein, & Bernt Schiele. (2017). Simple Does It: Weakly Supervised Instance and Semantic Segmentation. 1665–1674. 468 indexed citations breakdown →

15.

Perazzi, Federico, Anna Khoreva, Rodrigo Benenson, Bernt Schiele, & Alexander Sorkine‐Hornung. (2017). Learning Video Object Segmentation from Static Images. 3491–3500. 337 indexed citations breakdown →

16.

Oh, Seong Joon, Rodrigo Benenson, Anna Khoreva, et al.. (2017). Exploiting Saliency for Object Segmentation from Image Level Labels. UvA-DARE (University of Amsterdam). 126 indexed citations

17.

Khoreva, Anna, Rodrigo Benenson, Mohamed Omran, Matthias Hein, & Bernt Schiele. (2016). Weakly Supervised Object Boundaries. 29 indexed citations

18.

Khoreva, Anna, Fabio Galasso, Matthias Hein, & Bernt Schiele. (2015). Classifier based graph construction for video segmentation. IRIS Research product catalog (Sapienza University of Rome). 951–960. 30 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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