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In this paper, we propose a novel way to interpret text information byextracting visual feature presentation from multiple high-resolution andphoto-realistic synthetic images generated by Text-to-image GenerativeAdversarial Network (GAN) to improve the performance of image labeling.Firstly, we design a stacked Generative Multi-Adversarial Network (GMAN),StackGMAN++, a modified version of the current state-of-the-art Text-to-imageGAN, StackGAN++, to generate multiple synthetic images with various priornoises conditioned on a text. And then we extract deep visual features from thegenerated synthetic images to explore the underlying visual concepts for text.Finally, we combine image-level visual feature, text-level feature and visualfeatures based on synthetic images together to predict labels for images. Weconduct experiments on two benchmark datasets and the experimental resultsclearly demonstrate the efficacy of our proposed approach.

VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling