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Capturing the diversity of people in images is challenging: recent literaturetends to focus on diversifying one or two attributes, requiring expensiveattribute labels or building classifiers. We introduce a diverse people imageranking method which more flexibly aligns with human notions of peoplediversity in a less prescriptive, label-free manner. The Perception-AlignedText-derived Human representation Space (PATHS) aims to capture all or manyrelevant features of people-related diversity, and, when used as therepresentation space in the standard Maximal Marginal Relevance (MMR) rankingalgorithm, is better able to surface a range of types of people-relateddiversity (e.g. disability, cultural attire). PATHS is created in two stages.First, a text-guided approach is used to extract a person-diversityrepresentation from a pre-trained image-text model. Then this representation isfine-tuned on perception judgments from human annotators so that it capturesthe aspects of people-related similarity that humans find most salient.Empirical results show that the PATHS method achieves diversity better thanbaseline methods, according to side-by-side ratings from human annotators.

Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images