ICIMPACT 2027 (International Conference on Intelligent Multimedia, Physical-AI, and Cyber-Technologies) is an academic conference held in Bandung, Indonesia on 2027-02-03. The paper submission deadline is 2026-10-01. Acceptance notifications are sent on 2026-12-01.
Conference Tracks
ICIMPACT welcomes original research contributions in the following tracks (but not limited to):
Track 1 — Multimodal, Generative & Immersive Media Intelligence
Scope: AI methods and systems for understanding, retrieving, recommending, generating, and experiencing multimedia—covering both content intelligence and immersive/XR pipelines.
Focus areas:
Multimodal AI (vision–language–audio) for media understanding
Generative media (image/video/audio): controllable generation, editing, alignment
Video understanding, captioning, summarization, event/action recognition
Multimedia retrieval & semantic search (cross-modal indexing, ranking, evaluation)
Recommender systems for media/content (personalization, fairness, accountability)
3D media intelligence: tracking, reconstruction, scene understanding
Immersive media applications (AR/VR/MR), digital humans/avatars, motion intelligence
Multimodal interaction (voice/gesture/gaze) for immersive experiences
Real-time pipelines & QoE (latency, streaming, edge inference, efficiency)
Benchmarks, datasets, and evaluation protocols for media AI
Track 2 — Biomedical Multimedia Intelligence & Digital Health Systems
Scope: AI for biomedical media (medical imaging/video, biosignals, and clinical text) and digital health systems, aligned with Physical-AI (sensing, edge, XR) and cyber-technologies (privacy, security, auditability, provenance).
Focus areas:
Biomedical imaging & video AI: X-ray/CT/MRI/ultrasound, endoscopy, dermoscopy, WSI (classification, detection, segmentation, grading, prognosis)
Multimodal clinical learning: fusion of imaging + biosignals (ECG/PPG/EEG) + clinical text
Generative AI for medical media: denoising, super-resolution, reconstruction, modality translation, controlled augmentation
Physical-AI for healthcare: wearables/medical IoT, on-device/edge inference, real-time monitoring pipelines
Clinical XR/AR and immersive health: training/simulation, guidance, rehabilitation/therapy systems
Trustworthy deployment: robustness/domain shift, calibration/uncertainty, bias/fairness, clinician-in-the-loop evaluation
Explainable AI (XAI) for clinical media: interpretable evidence, usability-oriented explanations
Cyber-tech for health media: de-identification, consent/access control, audit logs, provenance/watermarking, integrity verification
System integration: clinical decision support, telemedicine workflows, reliability and latency constraints
Track 3 — Digital Business Platforms, Governance & Responsible AI
Scope: Engineering and governance of digital platforms and AI products, focusing on lifecycle control, accountability, and compliance-by-design.
Focus areas:
Digital platform engineering (marketplaces, super-apps, fintech platforms)
Data governance for AI products (lineage, quality, access, policy)
AI governance & model risk management (monitoring, drift, auditability)
Trust & safety systems (moderation, recommender accountability, safety KPIs)
MLOps/LLMOps governance (deployment control, logging, incident playbooks)
RegTech / compliance-by-design in digital systems
Digital identity, access management, and consent for platforms
Responsible AI: fairness, transparency, accountability, human oversight
Track 4 — Cyber-Technologies for Trustworthy AI & Secure Digital Media
Scope: Security, authenticity, and resilience for AI-enabled systems and digital media—protecting models, data, and pipelines against attacks and misuse.
Focus areas:
Deepfake detection & media forensics
Provenance, watermarking, authenticity verification
Adversarial ML: evasion/poisoning defenses, model theft protection
Privacy-preserving analytics for users/media (e.g., federated learning where relevant)
Secure pipelines: integrity, secure storage/streaming, tamper-evident audit logs
Threat/anomaly detection in digital platforms and AI systems
Security governance for AI-enabled systems (controls, risk management, incident response)
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