.png){kind=icon}
.png){kind=icon}
Return
Same-different conceptualization: a machine vision perspective
.png){kind=icon}
OA
AI
DOI：10.1016/j.cobeha.2020.08.008.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The goal of this review is to bring together material from cognitive psychology with recent machine vision studies to identify plausible neural mechanisms for visual same-different discrimination and relational understanding. We highlight how developments in the study of artificial neural networks provide computational evidence implicating attention and working memory in the ascertaining of visual relations, including same-different relations. We review some recent attempts to incorporate these mechanisms into flexible models of visual reasoning. Particular attention is given to recent models jointly trained on visual and linguistic information. These recent systems are promising, but they still fall short of the biological standard in several ways, which we outline in a final section.
Keywords:
VARIABILITY DISCRIMINATION
HIERARCHICAL-MODELS
ARCHITECTURE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
1.3K
Citations:
6.6K