会議情報

ICPR 2027: International Conference on Pattern Recognition

投稿締切日:
2027-03-01 残り 106 日
通知日:
2027-05-31
開催日:
2027-10-04
開催地:
Online
開催回数:
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CCF: C   ICORE: B   QUALIS: A1   閲覧: 658052   フォロー: 386   参加: 74
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会伴インデックス (CP-I)

89.7 / 100
全 5,777 件中 第 83 位 · 上位 2%

コンピュータビジョン・パターン認識 分野 247 件中 第 2 位

学術的評価 (35%)
100
投稿の選択性 (20%)
78
開催回数 (20%)
92
コミュニティの注目度 (10%)
79
公開情報の充実度 (15%)
85

使用した入力: 収録ランク：CCF C, ICORE B, QUALIS A1 · 採択率：30%（記録のある 4 回の平均） · 確認できる開催回数：29 · 会伴でフォローしている研究者：386 人 · 過去 24 か月にこのページを開いた研究者：25 人

公開情報で不足しているもの: 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 100% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-11-15

これらの機関の研究者がこの会議をフォローしています

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

研究者が自身のプロフィールに入力した所属に基づいています。当サイトでは確認していません。

論文募集

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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採択率

平均採択率: 30% 4 年間 (2000–2006).

年投稿数採択数採択率(%)
2006220030814%
2004178132118%
2002124025020.2%
2000143897367.7%

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関連会議

関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
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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