Recursiveness in Multimodal Generative Artificial Intelligence

The process is repeated in a loop. For the extraction of the description was used GPT-4o and for the generation of the images DALL-E3.

Description The dataset contains experiments of applying the RMC of length 40 generations from images that contains elements of the following categories: apples, elephants, fire-hydrants, persons, toilets, and trains. In total, there are 10 RMC loops per category (40*10*6 = 2,400 images) and the comparison between the images and descriptions using the metrics LPIPS VGG, TF-IDF, BERT tokenizer, BLIP. 1_coco_dataset: information from COCO images of each category 2_categories: loops of each category results_{category} experiments results_dall-e-3_hd_{style}_{category} -> hd (high definition), style (vivid or natural) -> all the experiments of that {style} and {category} results_all.xlsx -> similarity with metrics LPIPS, TF-IDF, BLIP, BERT aggregated experiments {date}_{style}_{coco_id} -> each experiment from a coco image imgs -> images imgs_resized -> resized images experiment.json -> json with data of the experiment (description, number of generations, etc.) results.xlsx -> metrics of each individual loop images_all.xlsx -> summary of all the images generated and their descriptions inter-experiments_results.xlsx -> aggregated results of inter-trajectory experiments of this {category} intra-experiments_results.xlsx -> aggregated results of intra-trajectory experiments of this {category} 3_combined results: combined results of all categories rsults_hd_labels_{style} -> all the results from images generated with the same style (vivid or natural) images_all.xlsx -> summary of all images inter-exeriments_results_all.xlsx intra-experiments_results_all.xlsx results_all_labels.xlsx -> summary of results per {category} 4_different_styles -> experiments comparing styles (natural and vivid) Paper Paper: Cite: @misc{}