Computer Vision: Cognitive Models for Visual Commonsense
| AUTHOR | Zhu, Yixin; Zhu, Song-Chun |
| PUBLISHER | Springer (01/03/2026) |
| PRODUCT TYPE | Hardcover (Hardcover) |
This volume on visual commonsense reasoning, part of a comprehensive three-volume series, presents a computational framework for bridging the gap between modern computer vision capabilities and human-like visual understanding. While current AI systems excel at pattern recognition tasks, they often lack the sophisticated reasoning capabilities that humans demonstrate effortlessly in understanding and interacting with their environment. This work addresses this limitation by integrating physical, social, and abstract reasoning within a unified computational framework.
The volume is organized into three parts. The first part establishes the theoretical foundations of visual commonsense through a systematic examination of physical understanding, including affordances, intuitive physics, causality, and tool use. These components form the basis for understanding how objects and environments behave and interact. The second part delves into social reasoning aspects, exploring intent, theory of mind, and nonverbal communication - crucial capabilities for AI systems to interpret and predict human behavior. The third part investigates abstract visual reasoning, examining higher-level cognitive capabilities. Drawing from cognitive science, computer vision, and artificial intelligence, this work:- Provides a systematic treatment of visual commonsense ranging from foundational theories to practical implementations
- Introduces computational frameworks integrating multiple forms of reasoning
- Demonstrates applications through extensive examples and case studies
- Highlights current challenges and future directions in developing human-like visual AI
This carefully crafted volume serves as an invaluable resource for researchers, graduate students, and practitioners in computer vision, artificial intelligence, cognitive science, and related fields. It offers both theoretical insights and practical guidance for developing AI systems with more sophisticated visual understanding capabilities, moving closer to human-like visual intelligence.
This volume on visual commonsense reasoning, part of a comprehensive three-volume series, presents a computational framework for bridging the gap between modern computer vision capabilities and human-like visual understanding. While current AI systems excel at pattern recognition tasks, they often lack the sophisticated reasoning capabilities that humans demonstrate effortlessly in understanding and interacting with their environment. This work addresses this limitation by integrating physical, social, and abstract reasoning within a unified computational framework.
The volume is organized into three parts. The first part establishes the theoretical foundations of visual commonsense through a systematic examination of physical understanding, including affordances, intuitive physics, causality, and tool use. These components form the basis for understanding how objects and environments behave and interact. The second part delves into social reasoning aspects, exploring intent, theory of mind, and nonverbal communication - crucial capabilities for AI systems to interpret and predict human behavior. The third part investigates abstract visual reasoning, examining higher-level cognitive capabilities. Drawing from cognitive science, computer vision, and artificial intelligence, this work:- Provides a systematic treatment of visual commonsense ranging from foundational theories to practical implementations
- Introduces computational frameworks integrating multiple forms of reasoning
- Demonstrates applications through extensive examples and case studies
- Highlights current challenges and future directions in developing human-like visual AI
This carefully crafted volume serves as an invaluable resource for researchers, graduate students, and practitioners in computer vision, artificial intelligence, cognitive science, and related fields. It offers both theoretical insights and practical guidance for developing AI systems with more sophisticated visual understanding capabilities, moving closer to human-like visual intelligence.