VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks

7 Oct 2020  ·  Soyeon Caren Han, Siqu Long, Siwen Luo, Kunze Wang, Josiah Poon ·

Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.

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Results from the Paper

Ranked #24 on Text-to-Image Generation on COCO (Inception score metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Text-to-Image Generation COCO StackGAN + VICTR Inception score 10.38 # 24
Text-to-Image Generation COCO DM-GAN + VICTR FID 32.37 # 60
Inception score 32.37 # 5
Text-to-Image Generation COCO AttnGAN + VICTR FID 29.26 # 58
Inception score 28.18 # 11