LLMs achieve higher accuracy than humans on compositional imagery tasks previously argued to require pictorial representations, supporting emergent propositional mental imagery in AI.
Learning image embeddings using convolutional neural networks for improved multi-modal semantics
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A literature survey that categorizes high-level abstract concept image classification tasks in CV into semantic clusters and identifies persistent challenges and opportunities for hybrid AI approaches.
citing papers explorer
-
Artificial Phantasia: Emergent Mental Imagery in Large Language Models
LLMs achieve higher accuracy than humans on compositional imagery tasks previously argued to require pictorial representations, supporting emergent propositional mental imagery in AI.
-
Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories
A literature survey that categorizes high-level abstract concept image classification tasks in CV into semantic clusters and identifies persistent challenges and opportunities for hybrid AI approaches.