Neural Image Inpainting Guided with Descriptive Text
Table 2: Numerical result of user study.
['Model', 'Naturalness', 'Semantic Consistency']
To evaluate the models from human perspective, we conduct a user study through a ranking game. We collect 100 test images with captions with object masks from the CUB validation dataset. For each image, four solutions from (1) Ground truth, (2) Our model, (3) PICNet, (4) CSA model are prepared. We randomly shuffle the four samples and recruit 20 volunteers to rank these four images according to naturalness and the semantic consistency to the text description. On the four solutions, we compute the average of the ranking score. According to the results, our model performs better than other models in terms of realistic, and significantly higher in semantic consistency.