SOCIETAL ATTRIBUTE NEUTRALIZER FOR DEBIASING CLIP

원문 (중국어)

The processes fine-tune vision-language models (VLMs) on large-scale image caption datasets to amend VLM text feature vectors of attribute-neutral descriptions given attribute-neutralization lists, such that the attribute-neutral descriptions are equidistant to those of attribute-specific descriptions using annotation-free debiasing loss without using attribute labels. Feature vectors for attribute-neutral descriptions can be debiased, whereas the attribute-specific descriptions retain the original information. One or more attribute groups can be used for the attribute-neutralization. There can be more than one VLM, such as for different human languages or different human cultures where some biasing can want to be retained. The processes can be applied to any image group, such as objects, animals, plants, rocks, or other object types, where there is at least one attribute group that contains at least two attributes for neutralization.