Conference Information

ICPR 2027: International Conference on Pattern Recognition

Submission Date:
2027-03-01 Due in 106 days
Notification Date:
2027-05-31
Conference Date:
2027-10-04
Location:
Online
Years:
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CCF: C   ICORE: B   QUALIS: A1   Viewed: 658049   Tracked: 386   Attend: 74
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Conference Partner Index (CP-I)

89.7 / 100
Ranked #83 of 5,777 conferences · Top 2%

#2 of 247 in Computer Vision & Pattern Recognition

Academic recognition (35%)
100
Submission selectivity (20%)
78
Editions held (20%)
92
Community attention (10%)
79
Public record completeness (15%)
85

Inputs used: Listed as CCF C, ICORE B, QUALIS A1 · Acceptance rate: 30% (mean of 4 editions on file) · Editions on record: 29 · Researchers following it here: 386 · Researchers who opened this page in the past 24 months: 25

Missing from the public record: Best-paper records (+2.3)
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Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-15

Call For Papers

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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Acceptance Ratio

Average acceptance rate: 30% over 4 years (2000–2006).

YearSubmittedAcceptedAccepted(%)
2006220030814%
2004178132118%
2002124025020.2%
2000143897367.7%

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