AJ Piergiovanni

839 total citations
13 papers, 127 citations indexed

About

AJ Piergiovanni is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, AJ Piergiovanni has authored 13 papers receiving a total of 127 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 3 papers in Signal Processing. Recurrent topics in AJ Piergiovanni's work include Multimodal Machine Learning Applications (8 papers), Human Pose and Action Recognition (6 papers) and Reinforcement Learning in Robotics (2 papers). AJ Piergiovanni is often cited by papers focused on Multimodal Machine Learning Applications (8 papers), Human Pose and Action Recognition (6 papers) and Reinforcement Learning in Robotics (2 papers). AJ Piergiovanni collaborates with scholars based in United States, United Kingdom and Germany. AJ Piergiovanni's co-authors include Anelia Angelova, Michael S. Ryoo, Anurag Arnab, Mostafa Dehghani, Weicheng Kuo, Chenyou Fan, Jenq–Neng Hwang, Alan H.B. Wu, Isaac Noble and Dahun Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cognition and International Journal of Computer Vision.

In The Last Decade

AJ Piergiovanni

11 papers receiving 126 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
AJ Piergiovanni United States	5	100	72	14	9	8	13	127
Hangjie Yuan China	7	140 1.4×	91 1.3×	9 0.6×	6 0.7×	4 0.5×	12	175
Gabriele Graffieti Italy	6	72 0.7×	93 1.3×	7 0.5×	12 1.3×	5 0.6×	9	133
Evgenii Zheltonozhskii Israel	5	105 1.1×	75 1.0×	5 0.4×	9 1.0×	5 0.6×	7	168
Evgeniya Ustinova Russia	2	113 1.1×	71 1.0×	22 1.6×	12 1.3×	4 0.5×	3	160
Zhongwei Cheng China	5	107 1.1×	76 1.1×	12 0.9×	7 0.8×	7 0.9×	9	116
Wendong Zhang China	6	128 1.3×	26 0.4×	16 1.1×	8 0.9×	14 1.8×	16	147
Dezhao Luo China	7	257 2.6×	150 2.1×	21 1.5×	12 1.3×	4 0.5×	10	283
Kenji Okuma Canada	3	119 1.2×	51 0.7×	13 0.9×	4 0.4×	5 0.6×	5	133
Davide Testuggine United States	2	37 0.4×	60 0.8×	9 0.6×	12 1.3×	2 0.3×	2	88

Countries citing papers authored by AJ Piergiovanni

Since Specialization

Citations

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

Fields of papers citing papers by AJ Piergiovanni

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of AJ Piergiovanni

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

Kim, Dahun, et al.. (2025). VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models. 29060–29070.

Piergiovanni, AJ, et al.. (2024). Mirasol3B: A Multimodal Autoregressive Model for Time-Aligned and Contextual Modalities. 26794–26804. 4 indexed citations

Piergiovanni, AJ. (2024). AViD Dataset: Anonymized Videos from Diverse Countries. Neural Information Processing Systems. 33. 16711–16721.

Piergiovanni, AJ, et al.. (2024). SLVP: Self-Supervised Language-Video Pre-Training for Referring Video Object Segmentation. 507–517. 2 indexed citations

Piergiovanni, AJ, Weicheng Kuo, & Anelia Angelova. (2023). Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning. 2214–2224. 28 indexed citations

Ryoo, Michael S., AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, & Anelia Angelova. (2021). TokenLearner: Adaptive Space-Time Tokenization for Videos. Neural Information Processing Systems. 34. 52 indexed citations

Jern, Alan, et al.. (2021). A computational framework for understanding the roles of simplicity and rational support in people's behavior explanations. Cognition. 210. 104606–104606. 1 indexed citations

Piergiovanni, AJ, Anelia Angelova, & Michael S. Ryoo. (2021). Tiny Video Networks. SHILAP Revista de lepidopterología. 3(1). 12 indexed citations

Piergiovanni, AJ, Anelia Angelova, & Michael S. Ryoo. (2020). Differentiable Grammars for Videos. Proceedings of the AAAI Conference on Artificial Intelligence. 34(7). 11874–11881. 3 indexed citations

10.

Piergiovanni, AJ, et al.. (2019). Model-Based Robot Imitation with Future Image Similarity. International Journal of Computer Vision. 128(5). 1360–1374. 3 indexed citations

11.

Piergiovanni, AJ & Michael S. Ryoo. (2018). Learning Shared Multimodal Embeddings with Unpaired Data.. arXiv (Cornell University). 2 indexed citations

12.

Wu, Alan H.B., AJ Piergiovanni, & Michael S. Ryoo. (2018). Action-Conditioned Convolutional Future Regression Models for Robot Imitation Learning. 518. 2116–21162. 2 indexed citations

13.

Piergiovanni, AJ, Chenyou Fan, & Michael S. Ryoo. (2017). Title Learning Latent Subevents in Activity Videos Using Temporal Attention Filters. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 18 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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