Roei Herzig

634 total citations
13 papers, 239 citations indexed

About

Roei Herzig is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics. According to data from OpenAlex, Roei Herzig has authored 13 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 1 paper in Biophysics. Recurrent topics in Roei Herzig's work include Multimodal Machine Learning Applications (6 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Roei Herzig is often cited by papers focused on Multimodal Machine Learning Applications (6 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Roei Herzig collaborates with scholars based in United States, Israel and United Kingdom. Roei Herzig's co-authors include Trevor Darrell, Amir Globerson, Gal Chechik, Anna Rohrbach, Amir Bar, Colorado Reed, Xin Wang, Karttikeya Mangalam, Leonid Karlinsky and Rogério Feris and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Neural Information Processing Systems.

In The Last Decade

Roei Herzig

11 papers receiving 234 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Roei Herzig United States	8	180	115	17	16	13	13	239
Seungeui Lee South Korea	4	178 1.0×	131 1.1×	17 1.0×	44 2.8×	15 1.2×	6	250
Ziteng Gao China	4	140 0.8×	71 0.6×	26 1.5×	22 1.4×	8 0.6×	8	182
Yiru Wang China	7	110 0.6×	155 1.3×	18 1.1×	11 0.7×	6 0.5×	16	238
Kean Chen China	6	149 0.8×	95 0.8×	17 1.0×	23 1.4×	4 0.3×	8	229
Fangyi Chen United States	5	163 0.9×	127 1.1×	22 1.3×	23 1.4×	6 0.5×	7	228
Shell Xu Hu United Kingdom	6	86 0.5×	125 1.1×	16 0.9×	8 0.5×	7 0.5×	9	186
Yu-Ying Yeh United States	7	271 1.5×	79 0.7×	16 0.9×	24 1.5×	37 2.8×	10	325
Ajoy Mondal India	11	203 1.1×	56 0.5×	48 2.8×	19 1.2×	8 0.6×	26	258
Akshay Raj Dhamija United States	7	127 0.7×	182 1.6×	7 0.4×	18 1.1×	8 0.6×	10	258

Countries citing papers authored by Roei Herzig

Since Specialization

Citations

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

Fields of papers citing papers by Roei Herzig

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roei Herzig

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

All Works

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13 of 13 papers shown

1.

Darrell, Trevor, et al.. (2025). In-Context Learning Enables Robot Action Prediction in LLMs. 8972–8979.

2.

Wang, Xudong, et al.. (2024). Unsupervised Universal Image Segmentation. 22744–22754. 14 indexed citations

3.

Darrell, Trevor, et al.. (2024). Compositional Chain-of-Thought Prompting for Large Multimodal Models. 14420–14431. 21 indexed citations

4.

Subramanian, Sanjay, et al.. (2024). TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering. 9740–9766. 3 indexed citations

5.

Herzig, Roei, Assaf Arbelle, Leonid Karlinsky, et al.. (2024). PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data. 6789–6801. 2 indexed citations

6.

Arbelle, Assaf, et al.. (2024). Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning. 22124–22153.

7.

Herzig, Roei, Leonid Karlinsky, Assaf Arbelle, et al.. (2023). Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs. 14077–14098. 6 indexed citations

8.

Arbelle, Assaf, Sivan Harary, Eli Schwartz, et al.. (2023). Teaching Structured Vision & Language Concepts to Vision & Language Models. 2657–2668. 19 indexed citations

9.

Harary, Sivan, Eli Schwartz, Assaf Arbelle, et al.. (2022). Unsupervised Domain Generalization by Learning a Bridge Across Domains. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5270–5280. 18 indexed citations

10.

Bar, Amir, Xin Wang, Colorado Reed, et al.. (2022). DETReg: Unsupervised Pretraining with Region Priors for Object Detection. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14585–14595. 80 indexed citations

11.

Herzig, Roei, Karttikeya Mangalam, Amir Bar, et al.. (2022). Object-Region Video Transformers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3138–3149. 51 indexed citations

12.

Herzig, Roei, et al.. (2018). Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. Neural Information Processing Systems. 31. 7211–7221. 18 indexed citations

13.

Herzig, Roei, et al.. (2018). Classifying Collisions with Spatio-Temporal Action Graph Networks.. 7 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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