학회 정보

ICPR 2027: International Conference on Pattern Recognition

투고 마감일:
2027-03-01 106일 남음
통보일:
2027-05-31
개최일:
2027-10-04
개최지:
Online
개최 횟수:
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CCF: C   ICORE: B   QUALIS: A1   조회: 658050   팔로우: 386   참가: 74
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ICPR
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회반 지수 (CP-I)

89.7 / 100
전체 5,777개 중 83위 · 상위 2%

컴퓨터 비전·패턴 인식 분야 247개 중 2위

학술적 인정 (35%)
100
투고 선별성 (20%)
78
개최 횟수 (20%)
92
커뮤니티 관심도 (10%)
79
공개 자료 충실도 (15%)
85

사용한 입력: 수록 등급: CCF C, ICORE B, QUALIS A1 · 게재율: 30%(기록이 있는 4개 회차의 평균) · 확인되는 개최 횟수: 29 · 회반에서 팔로우 중인 연구자: 386명 · 지난 24개월 동안 이 페이지를 연 연구자: 25명

공개 자료에서 빠진 항목: 최우수 논문 기록 (+2.3)
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이 기관들의 연구자가 이 학회를 추적하고 있습니다

Tsinghua University 18
Beijing University of Posts and Telecommunications 15
Institute of Information Engineering, Chinese Academy of Sciences 15
Peking University 12
East China Normal University 11
Beihang University 10
Institute of Automation, Chinese Academy of Sciences 10
Sun Yat-sen University 9

연구자가 자신의 프로필에 입력한 소속을 기준으로 하며, 별도로 확인하지 않습니다.

논문 모집

ICPR 2027 (International Conference on Pattern Recognition) is a CCF C / ICORE B / QUALIS A1 conference held in Online on 2027-10-04. The paper submission deadline is 2027-03-01. Acceptance notifications are sent on 2027-05-31.

The International Conference on Pattern Recognition Virtual (ICPRv) is the virtual extension of the flagship physical conference of the International Association for Pattern Recognition, the 29th edition of ICPR. Like its physical counterpart, this fully virtual conference encompasses a wide range of topics where Pattern Recognition methods are applied in fields including Computer Vision, Machine Learning, Image Processing, Speech and Natural Language Processing, and Sensor Pattern Processing. The Inaugural ICPRv, to be held in 2027, offers an excellent platform for students, academics, and industry researchers to foster new ideas and collaborations. Unlike the face-to-face ICPR conferences, ICPRv will not have tracks and plenary sessions. Instead, all papers will be presented in two-hour thematic sessions of papers on the same topic. This will create sessions with a more focused and specialized workshop-like feel, with opportunities for discussion and interaction with the authors. Authors will identify their preferred thematic session topic when they submit their paper. Delegates can attend any and all of the live thematic sessions, which will also be recorded for the benefit of delegates wanting to attend simultaneous sessions or sessions at inconvenient times. As well as the traditional ICPR conference topics listed at the end of this CFP, we encourage papers on truly novel research problems. Examples include (but not restricted to): Universal Neuro-Symbolic Knowledge Engines: Foundation models still hallucinate and update knowledge poorly. New architectures should fuse LLMs with dynamic knowledge graphs to absorb rich documents, update world models without retraining, and enable provable reasoning over evolving scientific and enterprise data. Omnimodal Embodied World Models: Current research still treats vision, audio, and text separately. The next step is to build omnimodal world models that integrate physics and asynchronous IoT signals, enabling robots to understand real-world causal rules and transfer smoothly from simulation to deployment. Continuous-Time Fluid Architectures: Efficient AI still relies on discrete tokens and batching. The next frontier is continuous, asynchronous architectures that adapt compute to signal entropy, using little energy for simple data and deeper reasoning only for anomalies or complex patterns. Mechanistic Fairness and Provable Unlearning: Current trustworthy AI methods remain largely post-hoc. The next step is to combine mechanistic interpretability and algorithmic fairness so models can isolate and remove specific biases or sensitive content, with provable guarantees of privacy and fairness for critical domains such as law and medicine. Adversarial Co-Evolutionary Data Ecosystems: As generative AI advances, the generator–detector arms race is becoming unsustainable. A next step is closed-loop ecosystems where generators and forensic models co-evolve during training, embedding cryptographic provenance and robust invisible watermarks directly into generated outputs. Causal Discovery Foundation Models: Causal discovery remains an emerging field. The next leap is foundation models that can design experiments, intervene in simulations, and infer causal equations from observational data, moving beyond prediction to uncover the physical or economic laws governing a system. Hyper-Personalized Cognitive Digital Twins: Personalized systems remain separate from advanced LLM reasoning. The next step is privacy-preserving, edge-based cognitive twins that learn a user’s physiological, emotional, and behavioral patterns, anticipate complex needs, and keep sensitive biometric and behavioral data strictly on-device.
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게재율

평균 게재율: 30% 4년간 (2000–2006).

연도투고 수게재 수게재율(%)
2006220030814%
2004178132118%
2002124025020.2%
2000143897367.7%

이 항목을 본 사람들이 함께 본 항목

관련 학회

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
International Journal of Pattern Recognition & Artificial IntelligenceWorld Scientific0218-0014
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.5Elsevier0167-8655
CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
CIEEE Internet of Things Journal8.9IEEE2327-4662
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
CExpert Systems with Applications7.5Elsevier0957-4174
CIEEE Transactions on Big Data5.7IEEE2332-7790

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