Machine Intelligence Research
MIR专题"Special Issue on Subtle Visual Computing"现公开征集原创稿件，截稿日期为2025年6月30日。欢迎赐稿！
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Special Issue on Subtle Visual Computing
专题简介
Subtle visual signals, though often imperceptible to the human eye, contain subtle yet crucial information that can reveal hidden patterns within visual data. By applying advanced computer vision and representation learning techniques, we can unlock the potential of these signals to better understand and interpret complex environments. This ability to detect and analyze subtle signals has profound implications across various fields, e.g., (1) from medicine, where early identification of minute anomalies in medical imaging can lead to life-saving interventions, (2) from industry, where spotting micro-defects in production lines can prevent costly failures, (3) from affective computing, where understanding micro-expression, micro-gesture, and hidden physiological signals under human interaction scenarios can benefit the deception detection. In an era overwhelmed by information, the capacity to detect and decode these “subtle visual signals” offers a novel and powerful approach to anticipating trends, identifying emerging threats, and discovering new opportunities. These signals, often ignored or overlooked, may hold key insights into future developments across different societal contexts.
Although recent advances on subtle visual computing have demonstrated significant potential, several challenges persist in terms of effectiveness, robustness, and generalization. Specifically, these challenges include: (1) limited representation of subtle visual signals, (2) insufficient generalization ability, and (3) limited performance in multi-task and multimodal scenarios. This special issue seeks to develop innovative representation learning models specifically designed to capture and interpret subtle visual signals. By doing so, it will provide new ways of perceiving and acting on visual information, empowering decision-making in fields such as healthcare, industrial processes, and affective computing. Ultimately, we hope this special issue aspires to demonstrate how hidden visual cues, when properly decoded, can offer critical foresight and actionable insights in an increasingly complex and interconnected world.
征稿范围(包括但不限于)
Topics of interest include, but are not limited to:
1) Theoretical analysis of robustness, generalization, and interpretability in subtle visual computing;
2) Subtle visual signal magnification;
3) Image and video based camouflaged object detection;
4) Subtle visual anomaly detection in medicine, industry, and biometric system;
5) Subtle multimedia manipulation detection and localization;
6) High-quality image segmentation and matting;
7) Subtle human behavior understanding (e.g., micro-expression & micro-gesture analysis, deception detection);
8) Video-based subtle physiological signal measurement;
9) New synthesis models for subtle visual content generation;
10) Innovative learning strategies for multi-modal subtle visual representation learning;
11) Lightweight and general backbone designs for subtle visual computing;
12) Large-scale datasets specific to subtle visual computing (Data should be publicly available without requiring access permission from the PI, and any related codes should be open source);
13) Survey or technical review about recent advances on subtle visual computing.
投稿指南
1) 截稿日期：2025年6月30日
2) 投稿地址（已开通）：
https://mc03.manuscriptcentral.com/mir
投稿时，请在系统中选择：
“Step 6 Details & Comments: Special Issue and Special Section---Special Issue on Subtle Visual Computing”.
3) 投稿及同行评议指南:
Full length manuscripts and peer reviewing will follow the MIR guidelines. For details: https://www.springer.com/journal/11633
客座编委 (*primary contacts)
Asst. Prof. Zitong Yu*, Great Bay University, China.
E-mail: yuzitong@gbu.edu.cn
Prof. Sergio Escalera, University of Barcelona, Spain.
E-mail: sergio.escalera.guerrero@gmail.com
Prof. Deng-Ping Fan, Nankai University, China.
E-mail: fdp@nankai.edu.cn.
Prof. Björn Schuller, Technische Universität München, Germany & Imperial College London, UK.
E-mail: schuller@tum.de
Prof. Philip H. S. Torr, University of Oxford, UK.
E-mail: philip.torr@eng.ox.ac.uk
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