Inferring Offensiveness In Images From Natural Language Supervision

Schramowski, Patrick, Kersting, Kristian Artificial Intelligence 

Probing or fine-tuning (large-scale) pre-trained models results in state-of-the-art performance for many NLP tasks and, more recently, even for computer vision tasks when combined with image data. Unfortunately, these approaches also entail severe risks. In particular, large image datasets automatically scraped from the web may contain derogatory terms as categories and offensive images, and may also underrepresent specific classes. Consequently, there is an urgent need to carefully document datasets and curate their content. Unfortunately, this process is tedious and error-prone. We show that pre-trained transformers themselves provide a methodology for the automated curation of large-scale vision datasets. Based on human-annotated examples and the implicit knowledge of a CLIP based model, we demonstrate that one can select relevant prompts for rating the offensiveness of an image. Deep learning models yielded many improvements in several fields. Particularly, transfer learning from models pre-trained on large-scale supervised data has become common practice in many tasks both with and without sufficient data to train deep learning models. While approaches like semisupervised sequence learning (Dai & Le, 2015) and datasets such as ImageNet (Deng et al., 2009), especially the ImageNet-ILSVRC-2012 dataset with 1.2 million images, established pre-training approaches, in the following years, the training data size increased rapidly to billions of training examples (Brown et al., 2020; Jia et al., 2021), steadily improving the capabilities of deep models.