STNet: Selective Tuning of Convolutional Networks for Object Localization
By Mahdi Biparva, John K. Tsotsos. 2017
16 citations recorded by the source corpus.
Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feed-forward processing on the Abstraction of concepts form raw images, the inherent nature of feedback processing has remained computationally controversial. Inspired by the computational models of covert visual attention, we propose the Selective Tuning of Convolutional Networks (STNet). It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of convolutional networks. We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset. We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
- Thomas Serre
- Sven Eberhardt
- Drew Linsley
- Dan Scheibler
- Luc Van Gool
- Andrew Zisserman
- Cordelia Schmid
- Jitendra Malik
Citations in the corpus
- Global-and-local attention networks for visual recognition (2018, methodology): “In this work, spatial and feature-based attention are combined in a single learned mask used to modulate feature representations, as is typically done in state-of-the-art systems [4–7].”