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Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks
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Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks
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Text-to-Image translation has been an active area of research in the recent past. The ability for a network to learn the meaning of a sentence and generate an accurate image that depicts the sentence shows ability of the model to think more like humans. Popular methods on text to image translation make use of Generative Adversarial Networks (GANs) to generate high quality images based on text input, but the generated images don't always reflect the meaning of the sentence given to the model as input. We address this issue by using a captioning network to caption on generated images and exploit the distance between ground truth captions and generated captions to improve the network further. We show extensive comparisons between our method and existing methods.
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Cited by 1 Pith paper
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ORGAN: Object-Centric Representation Learning using Cycle Consistent Generative Adversarial Networks
A cycle-consistent GAN that translates between images and object lists matches state-of-the-art detection on synthetic scenes and detects low-contrast cells where slot-attention models fail.
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