Affordances Provide a Fundamental Categorization Principle for Visual Scenes
How do we know that a kitchen is a kitchen by looking? Relatively little is known about how we conceptualize and categorize different visual environments. Traditional models of visual perception posit that scene categorization is achieved through the recognition of a scene's objects, yet these models cannot account for the mounting evidence that human observers are relatively insensitive to the local details in an image. Psychologists have long theorized that the affordances, or actionable possibilities of a stimulus are pivotal to its perception. To what extent are scene categories created from similar affordances? Using a large-scale experiment using hundreds of scene categories, we show that the activities afforded by a visual scene provide a fundamental categorization principle. Affordance-based similarity explained the majority of the structure in the human scene categorization patterns, outperforming alternative similarities based on objects or visual features. We all models were combined, affordances provided the majority of the predictive power in the combined model, and nearly half of the total explained variance is captured only by affordances. These results challenge many existing models of high-level visual perception, and provide immediately testable hypotheses for the functional organization of the human perceptual system.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Visual Affordance and Function Understanding: A Survey
Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries. Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment. The…
Affordance DetectionScene UnderstandingSurveyMining Semantic Affordances of Visual Object Categories
Affordances are fundamental attributes of objects. Affordances reveal the functionalities of objects and the possible actions that can be performed on them. Understanding affordances is crucial for recognizing human acti…
Collaborative FilteringObjectObject-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings
Acquiring knowledge about object interactions and affordances can facilitate scene understanding and human-robot collaboration tasks. As humans tend to use objects in many different ways depending on the scene and the ob…
ObjectScene UnderstandingBlocks as Probes: Dissecting Categorization Ability of Large Multimodal Models
Categorization, a core cognitive ability in humans that organizes objects based on common features, is essential to cognitive science as well as computer vision. To evaluate the categorization ability of visual AI models…
Question AnsweringVisual Question AnsweringLearning Intermediate Features of Object Affordances with a Convolutional Neural Network
Our ability to interact with the world around us relies on being able to infer what actions objects afford -- often referred to as affordances. The neural mechanisms of object-action associations are realized in the visu…