Language-Based Image Editing with Recurrent Attentive Models
Table 4: The average rate of consistency with captions and image quality for our model and the baseline model respectively, averaged over 1,000 images. The average quality of 1,000 truth images from the data set is also provided for comparison.
['[EMPTY]', 'Our Model', 'BaseLine', 'Truth']
S4SS3SSS0Px4 Results Our model achieves better consistency with captions and also better image quality by making use of information in captions. As we can see, without text input, the baseline approach often colorizes images with the same color (in this dataset, most images are painted with purple, red or white), while our framework can generate flowers similar to their original colors which are specified in texts.