Sill-Net: Feature Augmentation with Separated Illumination Representation

• URL: http://arxiv.org/abs/2102.03539v1
• Date: Sat, 6 Feb 2021 09:00:10 GMT
• Title: Sill-Net: Feature Augmentation with Separated Illumination Representation
• Authors: Haipeng Zhang, Zhong Cao, Ziang Yan, Changshui Zhang
• Abstract summary: We propose a novel neural network architecture called Separating-Illumination Network (Sill-Net) Sill-Net learns to separate illumination features from images, and then during training we augment training samples with these separated illumination features in the feature space. Experimental results demonstrate that our approach outperforms current state-of-the-art methods in several object classification benchmarks.
• Score: 35.25230715669166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models. Especially for some rare illumination conditions, collecting sufficient training samples could be time-consuming and expensive. To solve this problem, in this paper we propose a novel neural network architecture called Separating-Illumination Network (Sill-Net). Sill-Net learns to separate illumination features from images, and then during training we augment training samples with these separated illumination features in the feature space. Experimental results demonstrate that our approach outperforms current state-of-the-art methods in several object classification benchmarks.

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