Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published 2007 | Submitted
Journal Article Open

Attention in hierarchical models of object recognition

Abstract

Object recognition and visual attention are tightly linked processes in human perception. Over the last three decades, many models have been suggested to explain these two processes and their interactions, and in some cases these models appear to contradict each other. We suggest a unifying framework for object recognition and attention and review the existing modeling literature in this context. Furthermore, we demonstrate a proof-of-concept implementation for sharing complex features between recognition and attention as a mode of top-down attention to particular objects or object categories.

Additional Information

Copyright © 2007 Elsevier. Thomas Serre and Tomaso Poggio collaborated on parts of the work on sharing features between object detection and top-down attention. We would like to thank Karen F. Bernhardt-Walther, Kerstin Preuschoff, and Daniel Simons for helpful discussions and feedback on versions of the manuscript. This work was funded by DARPA, the NSF, the NIH, the NIMH, the ONR, the Keck Foundation, and a Beckman Postdoctoral Fellowship to D.B.W.

Attached Files

Submitted - 559.pdf

Files

559.pdf
Files (2.2 MB)
Name Size Download all
md5:2f5cccf59bb2ac5d29235f775693bb96
2.2 MB Preview Download

Additional details

Created:
September 22, 2023
Modified:
October 23, 2023