Conference Information

IVPAI 2027: International Conference on Image, Video Processing and Artificial Intelligence

Submission Date:
2027-02-01 Due in 80 days
Notification Date:
2027-03-03
Conference Date:
2027-05-09
Location:
Singapore
Years:
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Conference Partner Index (CP-I)

51.9 / 100
Ranked #1,343 of 5,776 conferences · Top 24%

#58 of 247 in Computer Vision & Pattern Recognition #123 of 755 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
62
Community attention (10%)
38
Public record completeness (15%)
55

Inputs used: Editions on record: 9 · Researchers following it here: 13 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - 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 2026-11-13

Call For Papers

IVPAI 2027 (International Conference on Image, Video Processing and Artificial Intelligence) is an academic conference held in Singapore on 2027-05-09. The paper submission deadline is 2027-02-01. Acceptance notifications are sent on 2027-03-03.

IVPAI 2027 invites original and technically sound research contributions in image and video processing, computer vision, visual machine learning, and artificial intelligence. The conference preserves its strong foundations in image enhancement, video analysis, visual recognition, and scene understanding, while extending its scope to emerging topics such as visual foundation models, multimodal reasoning, 3D/4D spatial perception, embodied vision, and vision-language-action learning. We particularly encourage research that bridges visual perception and intelligent interaction—for example, learning from egocentric video, understanding spatial and temporal dynamics, visual perception for robotic manipulation and navigation, and world models grounded in image or video data. Work on traditional imaging and vision remains equally welcome; an embodied AI component is not required. Topics of interest include, but are not limited to, the following six tracks. Contributions involving robotics or embodied AI should have a substantive image/video, computer-vision, or visual-learning component. Track 1: Image, Video Processing and Computational Imaging Image enhancement, denoising, deblurring, and restoration Image and video super-resolution and reconstruction Video enhancement, stabilization, and frame interpolation Image/video compression, coding, and quality assessment Computational photography and intelligent imaging systems Low-light, adverse-weather, and challenging-condition imaging Multispectral, hyperspectral, depth, and event-based imaging Visual signal processing and efficient image/video representations Track 2: Computer Vision, 3D Perception and Scene Understanding Object detection, visual recognition, and image segmentation Visual tracking and open-vocabulary object understanding 3D reconstruction, multi-view geometry, and depth estimation Neural scene representations and novel-view synthesis Optical flow, motion estimation, and dynamic scene analysis Human pose estimation and human–object interaction analysis Scene graphs, semantic scene understanding, and spatial representations Multi-camera and cross-view visual perception Track 3: Visual Learning and Video Understanding Deep learning and representation learning for visual data Self-supervised, few-shot, continual, and transfer learning in vision Spatiotemporal learning and long-form video understanding Action recognition, event detection, and action anticipation Egocentric video analysis and human activity understanding Motion analysis, trajectory modeling, and behavior prediction Robust, efficient, and domain-adaptive visual learning Open-world visual learning, video anomaly detection, and benchmarking Track 4: Generative AI, Vision Foundation Models and Multimodal Intelligence Image and video generation, synthesis, editing, and restoration Diffusion and other generative models for visual content Vision foundation models and efficient model adaptation Vision–language models and multimodal representation learning Visual question answering, grounding, and semantic reasoning Controllable image/video generation and temporal consistency Video-based world modeling and visual future prediction Synthetic visual data, generative model evaluation, and content authenticity Track 5: Embodied Vision and Robot Perception Active visual perception and embodied scene understanding Computer vision for robotic manipulation and interaction Visual navigation, localization, mapping, and SLAM Vision–language–action models and visually grounded planning 3D spatial reasoning, object affordances, and task-oriented perception Vision-based imitation learning and visuomotor policy learning Egocentric perception for embodied agents and autonomous systems Visual world models, predictive simulation, and sim-to-real adaptation Multisensory robot perception and closed-loop visual feedback Track 6: Intelligent Visual Systems and Applications Industrial visual inspection and defect detection Medical, biomedical, and healthcare image/video analysis Remote sensing, geospatial imaging, and earth observation Autonomous driving and intelligent transportation perception Intelligent video surveillance and safety monitoring Visual interfaces, augmented reality, and human–computer interaction Agricultural vision and environmental monitoring Real-time, edge, and resource-efficient visual AI systems Vision applications for drones, smart devices, and autonomous platforms
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