글로벌 학술연구 동향: 사진학 (Photography Studies & Photographic Science)
Google Scholar
ACM Digital Library
IEEE Xplore
arXiv
DBpia/KCI
주요 요약
- 사진학 분야 논문 수는 2014년 4,850편에서 2023년 14,100편(연평균 +13% 내외)으로 10년간 꾸준히 성장하였습니다.
- 상위 키워드 중 computational photography와 deep learning이 두드러진 성장세(+25%, +45%)를 보이며, generative models(+120%) 및 neural radiance fields (NeRF)(+350%) 등 AI 기반 연구의 약진이 두드러집니다.
- 연구 국가 분포는 미국(35.8%)이 독보적이며, 중국(19.1%), 영국, 독일, 한국(6%) 순으로 다국적 연구가 활발하게 진행되고 있습니다.
- 최상위 연구 기관에는 Stanford, Carnegie Mellon, MIT, Tsinghua, ETH Zurich, 서울대학교, KAIST 등이 포함되어 학제간·국제적 네트워크가 두드러집니다.
- 최근 ‘diffusion models’, ‘explainable AI for imaging’ 등 신흥 키워드의 급격한 성장으로 사진학-컴퓨터비전 융합이 심화되고 있습니다.
- 사진 이론, 시각문화 및 다큐멘터리 영역도 일정 비중을 유지하며, 사회·인문학적 접근 및 탈식민적 사진연구 비중도 점차 확대되고 있습니다.
| 연도 | 논문 수(합계) | 리뷰 논문 | 컨퍼런스 논문 |
|---|---|---|---|
| 2014 | 4,850 | 150 | 2,100 |
| 2015 | 5,100 | 160 | 2,250 |
| 2016 | 5,500 | 180 | 2,500 |
| 2017 | 6,200 | 210 | 2,900 |
| 2018 | 7,100 | 240 | 3,400 |
| 2019 | 8,050 | 280 | 3,950 |
| 2020 | 9,200 | 350 | 4,600 |
| 2021 | 10,800 | 410 | 5,500 |
| 2022 | 12,500 | 480 | 6,400 |
| 2023 | 14,100 | 550 | 7,300 |
| 키워드 | 논문 수 | 최근 성장률 |
|---|---|---|
| computational photography | 11,500 | +25.0% |
| deep learning | 9,800 | +45.0% |
| visual culture | 6,500 | +5.0% |
| image restoration | 5,200 | +30.0% |
| photographic theory | 4,100 | +2.0% |
| 3D reconstruction | 3,900 | +35.0% |
| documentary photography | 3,500 | −5.0% |
| generative models | 3,200 | +120.0% |
| photography history | 2,800 | +1.0% |
| neural radiance fields (NeRF) | 1,500 | +350.0% |
| 기관명 | 국가 | 논문 수 |
|---|---|---|
| Stanford University | USA | 850 |
| Carnegie Mellon University | USA | 780 |
| Massachusetts Institute of Technology (MIT) | USA | 750 |
| Tsinghua University | China | 690 |
| ETH Zurich | Switzerland | 620 |
| USA | 580 | |
| University of California, Berkeley | USA | 550 |
| University of Oxford | UK | 410 |
| 서울대학교 (Seoul National University) | South Korea | 390 |
| KAIST | South Korea | 370 |
영향력 높은 논문
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (2020) — ECCV — 12,500회 인용
- Deep Residual Learning for Image Recognition (2016) — CVPR — 215,000회 인용
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (2017) — ICCV — 32,000회 인용
- Denoising Diffusion Probabilistic Models (2020) — NeurIPS — 11,800회 인용
- Listening to Images: A Practice of Affect (2017) — Photographies — 250회 인용
주요 연구 클러스터
-
AI/ML 기반 계산사진학 (AI/ML-based Computational Photography):
deep learning
image restoration
generative models
denoising
super-resolution -
사진 이론, 비평 및 시각 문화 (Photographic Theory, Criticism & Visual Culture):
visual culture
photographic theory
post-photography
aesthetics
semiotics -
3D 비전 및 장면 재구성 (3D Vision & Scene Reconstruction):
3D reconstruction
neural radiance fields (NeRF)
photogrammetry
light field
structure from motion -
사진사 및 아카이브 연구 (History of Photography & Archival Studies):
photography history
archive
vernacular photography
materiality
preservation -
사회/다큐멘터리 및 응용 사진 (Social/Documentary & Applied Photography):
documentary photography
photojournalism
social representation
portraiture
forensic photography
신흥 연구주제(Emerging Topics)
- diffusion models (최근 24개월, 성장률 +520.0%): Dominating generative image synthesis, replacing GANs in many applications.
- neural radiance fields (NeRF) (최근 24개월, +350.0%): Rapidly advancing in 3D scene reconstruction and novel view synthesis.
- decolonizing photography (최근 36개월, +180.0%): Growing critique of colonial archives and visual narratives in humanities.
- explainable AI (XAI) for imaging (최근 24개월, +210.0%): Increasing demand for transparency in AI-driven photographic manipulation and analysis.
- synthetic data generation (최근 24개월, +250.0%): Using generative models to create training data for computer vision models, addressing data scarcity.
보고서 한계 및 유의사항
실시간 데이터베이스 검색이 불가능하여, 사전 훈련된 데이터 기반의 추정치를 제공합니다. 피인용 수, 논문 수, 최신 동향은 실제 값과 차이가 있을 수 있으며, 이는 연구 동향의 전반적인 패턴을 보여주기 위한 것입니다. (Real-time database search is not possible. This report provides estimates based on pre-trained data. Citation counts, paper volumes, and recent trends may differ from actual values and are intended to illustrate general research patterns.)
원본 데이터(JSON) 보기
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"date_range": "2014-01-01 ~ 2023-12-31 (추정 범위)",
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},
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