---
title: "ECCV (A*): papers and universities"
description: "ECCV (European Conference on Computer Vision) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 4,301 of its papers from 2024–2026 (1,962 in 2024, 2 in 2025, 2,337 in 2026). By the countries of the authors' institutions, the most papers come from China (2,078), the United States (1,115) and South Korea (439)."
url: https://scholar4ai.com/proceedings/eccv
markdown_url: https://scholar4ai.com/proceedings/eccv.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ECCV (European Conference on Computer Vision) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 4,301 of its papers from 2024–2026 (1,962 in 2024, 2 in 2025, 2,337 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# ECCV: European Conference on Computer Vision — papers and institutions, 2024–2026

Papers of ECCV, European Conference on Computer Vision (CORE rank A*; field: Computer vision and multimedia computation), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- ECCV (European Conference on Computer Vision) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 4,301 of its papers from 2024–2026 (1,962 in 2024, 2 in 2025, 2,337 in 2026).
- By the countries of the authors' institutions, the most papers come from China (2,078), the United States (1,115) and South Korea (439).
- The institutions with the most papers are Tsinghua University (254), Zhejiang University (208) and Shanghai Jiao Tong University (192).
- Its most-cited paper of these years is “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information” (2024), with 4,836 citations.

## Papers per year

*ECCV papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 1,962 | 1,936 |
| 2025 | 2 | 2 |
| 2026 | 2,337 | 2,320 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S106296714](https://openalex.org/S106296714) | Papers of the volumes titled “Computer Vision – ECCV <year>” (LNCS), found by DOI through Crossref |

## Who publishes there

*Institutions with the most ECCV papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 254 |
| 2 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 208 |
| 3 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 192 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 169 |
| 5 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 166 |
| 6 | Hong Kong University of Science and Technology | 141 |
| 7 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 140 |
| 8 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 130 |
| 9 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 126 |
| 10 | Shanghai Artificial Intelligence Laboratory | 126 |

*Countries with the most ECCV papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 2,078 |
| 2 | United States | 1,115 |
| 3 | South Korea | 439 |
| 4 | Hong Kong | 414 |
| 5 | United Kingdom | 278 |
| 6 | Singapore | 273 |
| 7 | Germany | 259 |
| 8 | Australia | 203 |
| 9 | Switzerland | 140 |
| 10 | Canada | 135 |

*Keywords with the most ECCV papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 351 |
| 2 | diffusion models | 346 |
| 3 | 3D Gaussian Splatting | 219 |
| 4 | multimodal large language models | 207 |
| 5 | CLIP | 120 |
| 6 | autonomous driving | 120 |
| 7 | text-to-image generation | 114 |
| 8 | self-supervised learning | 111 |
| 9 | novel view synthesis | 110 |
| 10 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 108 |

## Most-cited papers

*Most-cited ECCV papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 4,836 | [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://doi.org/10.1007/978-3-031-72751-1_1) | 2024-10-25 | Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao |
| 2 | 2,082 | [Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection](https://doi.org/10.1007/978-3-031-72970-6_3) | 2024-11-22 | Shilong Liu, Zhaoyang Zeng, Tianhe Ren et al. |
| 3 | 712 | [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://doi.org/10.1007/978-3-031-72649-1_13) | 2024-09-29 | Hang Guo, Jinmin Li, Tao Dai et al. |
| 4 | 688 | [MobileNetV4: Universal Models for the Mobile Ecosystem](https://doi.org/10.1007/978-3-031-73661-2_5) | 2024-11-09 | Danfeng Qin, Chas Leichner, Manolis Delakis et al. |
| 5 | 488 | [MMBench: Is Your Multi-modal Model an All-Around Player?](https://doi.org/10.1007/978-3-031-72658-3_13) | 2024-10-01 | Yuan Liu, Haodong Duan, Yuanhan Zhang et al. |
| 6 | 339 | [VideoMamba: State Space Model for Efficient Video Understanding](https://doi.org/10.1007/978-3-031-73347-5_14) | 2024-10-28 | Kunchang Li, Xinhao Li, Yi Wang et al. |
| 7 | 313 | [Grounding Image Matching in 3D with MASt3R](https://doi.org/10.1007/978-3-031-73220-1_5) | 2024-11-02 | Vincent Leroy, Yohann Cabon, Jérôme Revaud |
| 8 | 279 | [DiffBIR: Toward Blind Image Restoration with Generative Diffusion Prior](https://doi.org/10.1007/978-3-031-73202-7_25) | 2024-11-20 | Xinqi Lin, Jingwen He, Ziyan Chen et al. |
| 9 | 257 | [Adversarial Diffusion Distillation](https://doi.org/10.1007/978-3-031-73016-0_6) | 2024-10-25 | Axel Sauer, Dominik Lorenz, Andreas Blattmann et al. |
| 10 | 255 | [DriveLM: Driving with Graph Visual Question Answering](https://doi.org/10.1007/978-3-031-72943-0_15) | 2024-11-28 | Chonghao Sima, Katrin Renz, Kashyap Chitta et al. |

## Cite this page

Scholar4AI. (2026). ECCV: European Conference on Computer Vision — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/eccv

```bibtex
@misc{scholar4ai-proceedings-eccv,
  author = {{Scholar4AI}},
  title = {ECCV: European Conference on Computer Vision — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/eccv}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).