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ICRMV 2027: International Conference on Robotics and Machine Vision

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ICRMV
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截稿日期:
2026-11-01 还有 46 天
通知日期:
2026-12-01
会议日期:
2027-03-19
会议地点:
Haining, China
届数:
浏览: 15637   关注: 0   参加: 0

会伴指数 (CP-I)

50.5 / 100
全站第 1,471 名 / 共 5,682 个会议 · 前 26%

机器人与控制 第 63 / 469 计算机视觉与模式识别 第 63 / 244

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
67
社区关注 (10%)
13
资料公开度 (15%)
55

用到的输入: 有据可查的届次：11 · 过去 24 个月打开过本页的研究者：5 人

公开资料里还缺: 历年录用率 (+4.5) · 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 45% —— 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 1.1 · 算于 2026-09-15

征稿

ICRMV 2027 (International Conference on Robotics and Machine Vision) is an academic conference held in Haining, China on 2027-03-19. The paper submission deadline is 2026-11-01. Acceptance notifications are sent on 2026-12-01.

The conference focuses on innovative and original research results in robotics, machine vision, and their integration through learning-based approaches. Both theoretical papers and experimental/simulation studies are welcome. Topics of interest include, but are not limited to: 1. Machine Learning in Robotics Supervised, unsupervised, and reinforcement learning for robotics Adaptive and autonomous robot control systems Robot learning from demonstration and imitation Learning-based motion planning and navigation Multi-robot learning and coordination Transfer learning and domain adaptation for robotic applications 2. Deep Learning in Robotics Deep reinforcement learning for robotic control Convolutional Neural Networks (CNNs) for robotic perception Recurrent Neural Networks (RNNs) for sequential robot tasks Deep learning for robot localization and mapping (SLAM) End-to-end learning for robotic manipulation and locomotion Self-supervised and semi-supervised learning for robotics 3. Machine Vision and Perception Image and video analysis for robotic applications 3D vision, structure from motion, and depth perception Visual tracking, object detection, and pose estimation Semantic and instance segmentation for scene understanding Vision-based manipulation, grasping, and tactile-visual integration Sensor fusion (vision, LiDAR, radar, IMU) and multi-modal perception 4. Robotics Fundamentals and Control Robot kinematics, dynamics, and motion planning Multi-robot systems, swarm robotics, and coordination Human-robot interaction and collaborative robotics Soft robotics, bio-inspired robotics, and reconfigurable robots Robot perception, state estimation, and sensor networks Real-time and embedded control architectures 5. Applications of Machine Learning and Vision in Robotics Healthcare, surgical, and rehabilitation robotics Industrial automation, manufacturing, and logistics Autonomous vehicles, drones, and intelligent transportation systems Service, social, and assistive robotics Agricultural, field, and forestry robotics Exploration, search and rescue, and planetary robotics 6. Theoretical Foundations and Real-World Deployments Advances in neural network architectures for robotic vision Statistical learning, scalability, and real-time efficiency Explainability, interpretability, and safety of robotic learning models Robustness, generalization, and domain adaptation in real-world environments Benchmarking, performance evaluation, and case studies of deployed systems Ethical considerations, legal frameworks, and human-centric design
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