Philip Torr

921 total citations · 1 hit paper
15 papers, 218 citations indexed

About

Philip Torr is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Philip Torr has authored 15 papers receiving a total of 218 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 2 papers in Media Technology. Recurrent topics in Philip Torr's work include Multimodal Machine Learning Applications (4 papers), Natural Language Processing Techniques (4 papers) and Speech and dialogue systems (3 papers). Philip Torr is often cited by papers focused on Multimodal Machine Learning Applications (4 papers), Natural Language Processing Techniques (4 papers) and Speech and dialogue systems (3 papers). Philip Torr collaborates with scholars based in United Kingdom, United States and Germany. Philip Torr's co-authors include Hengshuang Zhao, Yansong Tang, Zhao Yang, Kai Chen, Jiaqi Wang, Chris Russell, Paul Sturgess, Ľubor Ladický, Sunando Sengupta and Yalın Baştanlar and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and IEEE Robotics and Automation Letters.

In The Last Decade

Philip Torr

8 papers receiving 215 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.

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

2022 169 citations Zhao Yang, Jiaqi Wang et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers

Philip Torr

CVPR

AI

AE

MT

RNMI

Ziteng Gao China

Noel E. O’Connor Ireland

Mingzhen Huang United States

Abulikemu Abuduweili China

Thao Nguyen United States

Yuanfeng Ji China

Liang Han China

Chuanxia Zheng Singapore

Nuno C. Garcia Portugal

Jungbeom Lee South Korea

Ziteng Gao China

Philip Torr

140 ×0.8

CVPR

71 ×0.9

AI

22 ×1.1

AE

26 ×1.5

MT

9 ×0.6

RNMI

Citations per year, relative to Philip Torr Philip Torr (= 1×) peers Ziteng Gao

Countries citing papers authored by Philip Torr

Since Specialization

Citations

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

Fields of papers citing papers by Philip Torr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philip Torr

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

15 of 15 papers shown

1.

Torr, Philip, et al.. (2025). Text-guided camouflaged object detection. Pattern Recognition. 170. 112058–112058.

2.

Siarohin, Aliaksandr, et al.. (2025). Video Motion Transfer with Diffusion Transformers. 22911–22921. 1 indexed citations

3.

Tian, Yonglong, et al.. (2025). Vision-Language Models Do Not Understand Negation. 29612–29622.

4.

Xia, Yifan, et al.. (2025). Reimagining Safety Alignment with An Image. 9600–9614.

5.

Chen, Yanjun, Zecheng Zhang, Zhiqiang Xie, et al.. (2025). CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. 21607–21647.

6.

Xue, Yuan, et al.. (2025). Open-World Objectness Modeling Unifies Novel Object Detection. 30332–30342. 1 indexed citations

7.

Li, Rui, et al.. (2025). DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer. Research at the University of Copenhagen (University of Copenhagen). 1243–1252.

8.

Robinson, Luke, et al.. (2025). Select2Plan: Training-Free ICL-Based Planning Through VQA and Memory Retrieval. IEEE Robotics and Automation Letters. 10(11). 11267–11274.

9.

Chen, Shuo, et al.. (2025). Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?. 6000–6010.

10.

Zhang, Jiaming, Kunyu Peng, Junwei Zheng, et al.. (2024). RoDLA: Benchmarking the Robustness of Document Layout Analysis Models. 15556–15566. 2 indexed citations

11.

Li, Hang, et al.. (2024). Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation. 12006–12016. 4 indexed citations

12.

Deng, Jiankang, et al.. (2023). Linear Complexity Self-Attention with $3^{\text{rd}}$ Order Polynomials. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(11). 1–12. 4 indexed citations

13.

Sun, Shuyang, Xiaoyu Yue, Hengshuang Zhao, Philip Torr, & Song Bai. (2022). Patch-based Separable Transformer for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1–8. 5 indexed citations

14.

Yang, Zhao, Jiaqi Wang, Yansong Tang, et al.. (2022). LAVT: Language-Aware Vision Transformer for Referring Image Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18134–18144. 169 indexed citations breakdown →

15.

Ladický, Ľubor, Paul Sturgess, Chris Russell, et al.. (2010). Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction. University of Hertfordshire Research Archive (University of Hertfordshire). 104.1–104.11. 32 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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