会议信息

ICPR 2027: International Conference on Pattern Recognition

截稿日期:
2027-03-01 还有 106 天
通知日期:
2027-05-31
会议日期:
2027-10-04
会议地点:
Online
届数:
请登录查看会议网址
免费注册：查看官网链接、跟踪截稿日期，并接收邮件提醒。
注册 登录
加入日历: Google 日历 · Outlook / Apple 日历（.ics）
CCF: C   ICORE: B   QUALIS: A1   浏览: 658053   关注: 386   参加: 74
这一页信息有误？告诉我们
嵌入截止倒计时徽章
ICPR
用 API 获取这条数据
搜索与榜单列表完全无需凭证；本页的完整详情需要一把免费 API 密钥。详见开发者接入页。

会伴指数 (CP-I)

89.7 / 100
全站第 83 名 / 共 5,777 个会议 · 前 2%

计算机视觉与模式识别 第 2 / 247

学术认可 (35%)
100
投稿选择性 (20%)
78
会议传承 (20%)
92
社区关注 (10%)
79
资料公开度 (15%)
85

用到的输入: 收录等级：CCF C, ICORE B, QUALIS A1 · 录用率：30%（有记录的 4 届的均值） · 有据可查的届次：29 · 在会伴关注它的研究者：386 人 · 过去 24 个月打开过本页的研究者：25 人

公开资料里还缺: 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-11-15

征稿

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.
由 Admin Agent 最后更新于

录用率

平均录用率: 30% 4 年间 (2000–2006).

年份提交数录用数录用率(%)
2006220030814%
2004178132118%
2002124025020.2%
2000143897367.7%

看过这个的人也在看

相关会议

相关期刊

CCF全称影响因子出版商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

评论 0

暂无评论。

请登录后发表评论