Ventral-dorsal neural networks: Object detection via selective attention

Mohammad K. Ebrahimpour, Jiayun Li, Yen Yun Yu, Jackson L. Reese, Azadeh Moghtaderi, Ming Hsuan Yang, David C. Noelle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Deep Convolutional Neural Networks (CNNs) have been repeatedly proven to perform well on image classification tasks. Object detection methods, however, are still in need of significant improvements. In this paper, we propose a new framework called Ventral-Dorsal Networks (VDNets) which is inspired by the structure of the human visual system. Roughly, the visual input signal is analyzed along two separate neural streams, one in the temporal lobe and the other in the parietal lobe. The coarse functional distinction between these streams is between object recognition — the “what” of the signal – and extracting location related information — the “where” of the signal. The ventral pathway from primary visual cortex, entering the temporal lobe, is dominated by “what” information, while the dorsal pathway, into the parietal lobe, is dominated by “where” information. Inspired by this structure, we propose the integration of a “Ventral Network” and a “Dorsal Network”, which are complementary. Information about object identity can guide localization, and location information can guide attention to relevant image regions, improving object recognition. This new dual network framework sharpens the focus of object detection. Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches on PASCAL VOC 2007 by 8% (mAP) and PASCAL VOC 2012 by 3% (mAP). Moreover, a comparison of techniques on Yearbook images displays substantial qualitative and quantitative benefits of VDNet.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages986-994
Number of pages9
ISBN (Electronic)9781728119755
DOIs
Publication statusPublished - 2019 Mar 4
Event19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, United States
Duration: 2019 Jan 72019 Jan 11

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Conference

Conference19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
CountryUnited States
CityWaikoloa Village
Period19/1/719/1/11

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Ebrahimpour, M. K., Li, J., Yu, Y. Y., Reese, J. L., Moghtaderi, A., Yang, M. H., & Noelle, D. C. (2019). Ventral-dorsal neural networks: Object detection via selective attention. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (pp. 986-994). [8658799] (Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2019.00110