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.
Escalante, Dusan Misevic, Ulrich Steiner, and Isabelle Guyon
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
AMRL reaches state-of-the-art accuracy on apparent age estimation yet exhibits clear performance drops for Asian and African American groups due to inconsistent feature focus, showing that technical tweaks are not enough without diverse localized data.
citing papers explorer
-
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.
-
Apparent Age Estimation: Challenges and Outcomes
AMRL reaches state-of-the-art accuracy on apparent age estimation yet exhibits clear performance drops for Asian and African American groups due to inconsistent feature focus, showing that technical tweaks are not enough without diverse localized data.