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arXiv · 1605.00052

InterActive: Inter-Layer Activeness Propagation

Lingxi Xie·Liang Zheng·Jingdong Wang·Alan Yuille·Qi Tian
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Abstract

An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying high-level context and improving the descriptive power of low-level and mid-level neurons. Visualization indicates that neuron activeness can be interpreted as spatial-weighted neuron responses. We achieve state-of-the-art classification performance on a wide range of image datasets.

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Lingxi Xie, Liang Zheng, Jingdong Wang, Alan Yuille, Qi Tian. 2016-04-30. InterActive: Inter-Layer Activeness Propagation. https://arxiv.org/abs/1605.00052

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