Luca Weihs

1.3k total citations · 1 hit paper
23 papers, 488 citations indexed

About

Luca Weihs is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Luca Weihs has authored 23 papers receiving a total of 488 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 3 papers in Control and Systems Engineering. Recurrent topics in Luca Weihs's work include Multimodal Machine Learning Applications (12 papers), Human Pose and Action Recognition (8 papers) and Reinforcement Learning in Robotics (7 papers). Luca Weihs is often cited by papers focused on Multimodal Machine Learning Applications (12 papers), Human Pose and Action Recognition (8 papers) and Reinforcement Learning in Robotics (7 papers). Luca Weihs collaborates with scholars based in United States, Switzerland and Canada. Luca Weihs's co-authors include Aniruddha Kembhavi, Roozbeh Mottaghi, Ali Farhadi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matt Deitke, Ludwig Schmidt, Oscar Michel and Mathias Drton and has published in prestigious journals such as SHILAP Revista de lepidopterología, Biometrika and The Annals of Statistics.

In The Last Decade

Luca Weihs

21 papers receiving 475 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.

Objaverse: A Universe of Annotated 3D Objects

2023 236 citations Matt Deitke, Dustin Schwenk et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Luca Weihs United States	9	287	126	110	99	73	23	488
Thomas M. Strat United States	12	242 0.8×	150 1.2×	23 0.2×	26 0.3×	24 0.3×	27	452
Matthew D. Mullin United States	9	496 1.7×	152 1.2×	22 0.2×	18 0.2×	19 0.3×	11	657
Ioannis Ivrissimtzis United Kingdom	13	213 0.7×	24 0.2×	192 1.7×	150 1.5×	17 0.2×	58	423
Pulak Purkait India	12	360 1.3×	99 0.8×	32 0.3×	11 0.1×	23 0.3×	23	451
Andrei Zanfir Romania	8	637 2.2×	116 0.9×	210 1.9×	47 0.5×	93 1.3×	11	708
Omri Avrahami Israel	6	548 1.9×	121 1.0×	78 0.7×	169 1.7×	26 0.4×	9	694
P. Whaite Canada	6	312 1.1×	52 0.4×	52 0.5×	22 0.2×	25 0.3×	11	414
Jiazhao Zhang China	10	162 0.6×	75 0.6×	51 0.5×	19 0.2×	45 0.6×	15	297
Haiyong Jiang China	9	125 0.4×	45 0.4×	97 0.9×	47 0.5×	15 0.2×	22	397

Countries citing papers authored by Luca Weihs

Since Specialization

Citations

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

Fields of papers citing papers by Luca Weihs

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Weihs

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Weihs. A scholar is included among the top collaborators of Luca Weihs 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 Luca Weihs. Luca Weihs 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:

20 of 20 papers shown

1.

Yang, Yue, Ajay Patel, Matt Deitke, et al.. (2025). Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation. 17486–17505. 1 indexed citations

2.

Zhang, Zichen, et al.. (2024). Universal Visual Decomposer: Long-Horizon Manipulation Made Easy. 6973–6980. 4 indexed citations

3.

Yang, Yue, Fan-Yun Sun, Luca Weihs, et al.. (2024). Holodeck: Language Guided Generation of 3D Embodied AI Environments. 16277–16287. 10 indexed citations

4.

Ehsani, Kiana, Tanmay Gupta, Rose Hendrix, et al.. (2024). SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World. 16238–16250. 12 indexed citations

5.

Weihs, Luca, et al.. (2024). Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences. 16216–16226. 2 indexed citations

6.

Kembhavi, Aniruddha, et al.. (2024). Seeing the Unseen: Visual Common Sense for Semantic Placement. 16273–16283.

7.

Ehsani, Kiana, et al.. (2023). Towards Disturbance-Free Visual Mobile Manipulation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 5208–5220. 2 indexed citations

8.

Deitke, Matt, Dustin Schwenk, Jordi Salvador, et al.. (2023). Objaverse: A Universe of Annotated 3D Objects. 13142–13153. 236 indexed citations breakdown →

9.

Salvador, Jordi, et al.. (2023). Scene Graph Contrastive Learning for Embodied Navigation. 10850–10860. 7 indexed citations

10.

Weihs, Luca, et al.. (2022). Simple but Effective: CLIP Embeddings for Embodied AI. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14809–14818. 102 indexed citations

11.

Barber, Rina Foygel, et al.. (2022). Half-trek criterion for identifiability of latent variable models. The Annals of Statistics. 50(6). 2 indexed citations

12.

Jain, Unnat, et al.. (2021). GridToPix: Training Embodied Agents with Minimal Supervision. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 15121–15131. 10 indexed citations

13.

Jain, Unnat, et al.. (2021). GridToPix: Training Embodied Agents with Minimal Supervision. arXiv (Cornell University). 15141–15151. 2 indexed citations

14.

Zeng, Kuo-Hao, Luca Weihs, Ali Farhadi, & Roozbeh Mottaghi. (2021). Pushing it out of the Way: Interactive Visual Navigation. 9863–9872. 21 indexed citations

15.

Barber, Rina Foygel, Mathias Drton, & Luca Weihs. (2019). Identifiability of Linear Structural Equation Models [R package SEMID version 0.3.2].

16.

Weihs, Luca, Aniruddha Kembhavi, Kiana Ehsani, et al.. (2019). Learning Generalizable Visual Representations via Interactive Gameplay. arXiv (Cornell University). 5 indexed citations

17.

Weihs, Luca, Mathias Drton, & Nicolai Meinshausen. (2018). Symmetric rank covariances: a generalized framework for nonparametric measures of dependence. Biometrika. 105(3). 547–562. 31 indexed citations

18.

Weihs, Luca, et al.. (2017). Determinantal Generalizations of Instrumental Variables. SHILAP Revista de lepidopterología. 6(1). 3 indexed citations

19.

Weihs, Luca & Oren Etzioni. (2017). Learning to Predict Citation-Based Impact Measures. 1–10. 18 indexed citations

20.

Drton, Mathias & Luca Weihs. (2016). Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition. Scandinavian Journal of Statistics. 43(4). 1035–1045. 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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