3/7/2023 0 Comments Text on image designAnnotation MethodologyĬertain styles placed motifs, or distinctive details, within the generation that made the style come through irrespective of the subject. The remaining 33 were styles that were balanced for representation on both sides of the following partitions: abstract-figurative, Western and non-Western, premodern and modern. Eight were general mediums of visual art. In Experiment 4, we run 51 styles which are listed in the table above. Annotators evaluated 143 grids of generations for significantly better generations as well significantly worse generations. For Experiment #1, annotators judged 3x3 grids where generations from different prompt permutations were arranged randomly. All annotators were compensated $20/hour for however long it took them to complete the task. We explained that they did not have to judge whether or not the generation represented the subject or the style they just had to report whether there were generations that were significantly different from the rest. One combination was taken out owing to inappropriate content.Īnnotators were asked to note which images in the grid were either significantly better generations or significantly worse generations. The 144 combinations were presented in 3x3 grids, where the prompt permutations were randomly arranged to prevent any effect from ordering. We had each combination of subject and style rated by two people with who had backgrounds in media arts and art practice respectively. We balanced for time periods (with 6 styles predating the 20th century, and 6 styles from the 20th and 21st century). Specifically, we chose four abstract styles, four figurative styles, and four aesthetics related to the digital age. These styles likewise varied in whether they represented the world in an abstract or figurative manner. ![]() Likewise, we chose 12 styles spanning different time periods, cultural traditions, and aesthetics: Cubist, Islamic geometric art, Surrealism, action painting, Ukiyo-e, ancient Egyptian art, High Renaissance, Impressionism, cyberpunk, unreal engine, Disney, VSCO. , 2013) Our set of abstract subjects averaged 2.12 on a scale from one to five (one being most abstract), and our set of concrete subjects averaged 4.80. We decided on whether a subject fell into the abstract or concrete category based upon ratings taken from a dataset of concreteness values. These subjects additionally were balanced for how abstract or concrete they were as a concept as well as for positive and negative sentiment. These subjects were chosen for their universality across media and across cultures. ![]() We chose the following subjects: love, hate, happiness, sadness, man, woman, tree, river, dog, cat, ocean, and forest. To study prompt permutations, we generated 12x12x9 images from VQGAN+CLIP (pretrained on Imagenet with the 16384 codebook) according to a combination of subjects, styles, and prompt permutations. The authors of DALL-E demonstrated how the model could handle image operations, perform style transfer, and produce novel combinations of elements. DALL-E learned a transformer that autoregressively predicted text and image tokens together in one sequence. , 2021), one of the state of the art models for text-to-image generation. CLIP demonstrated that the model was able to learn ”visual concepts…enabling zero-shot transfer of the model” on various tasks such as OCR, geo-localization, and others. , 2021) CLIP was trained on an Internet scale size dataset of 400 million image and text pairs to optimize a contrastive objective that pushed image and text embeddings closer towards one another. from OpenAI introduced CLIP (Contrastive Language-Image Pre-training), a method for learning multimodal image representations. ![]() Recent work from computer vision has focused on learning text and image together in generative models, by coupling the two modalities through optimization. This interest in involving natural language as a form of interaction with generative models has recently found success in computer vision.
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