Observing that the original semantic pool is comprised of unmodified specific class names, we correspondingly construct a conjugated semantic pool (CSP) consisting of modified superclass names, each serving as a cluster center for samples sharing similar properties across different categories.

A straightforward pipeline for zero-shot out-of-distribution (OOD) detection involves selecting potential OOD labels from an extensive semantic pool and then leveraging a pre-trained vision-language model to perform classification on both in-distribution (ID) and OOD labels. In this paper, we theorize that enhancing performance requires expanding the semantic pool, while increasing the expected probability of selected OOD labels being activated by OOD samples, and ensuring low mutual dependence among the activations of these OOD labels. A natural expansion manner is to adopt a larger lexicon; however, the inevitable introduction of numerous synonyms and uncommon words fails to meet the above requirements, indicating that viable expansion manners move beyond merely selecting words from a lexicon. Since OOD detection aims to correctly classify input images into ID/OOD class groups, we can "make up" OOD label candidates which are not standard class names but beneficial for the process. Observing that the original semantic pool is comprised of unmodified specific class names, we correspondingly construct a conjugated semantic pool (CSP) consisting of modified superclass names, each serving as a cluster center for samples sharing similar properties across different categories. Consistent with our established theory, expanding OOD label candidates with the CSP satisfies the requirements and outperforms existing works by 7.89% in FPR95.

More recent works introduced advanced multi-criteria optimization models [12, 21, 27] that can express more accurately several established cartographic principles, but still with the aim of a full automation of the map labeling process. While progress is made by incorporating more comprehensive cartographic rules for label placement, none of the above approaches includes decisions made by human experts - other than setting preferences, parameters, and priorities in the different scoring functions that control a single optimization run of the respective algorithm. A notable exception is the UserHints framework [7], where human interaction was integrated into solving the label number maximization problem in a fixed-position point labeling setting. In that system, two heuristic methods were implemented as labeling algorithms, and hence the evaluation could not assess the deviation from optimal solutions with respect to the objective function. Moreover, the authors did not consider the stability of the labeling under user interaction. Beyond the label placement problem, interactive optimization [22] and human-guided search [16] are of course techniques that are of general interest and more broadly applicable. 2 Popular GIS software like Mapbox 1, ArcGIS Pro 2, or QGIS 3 also provide labeling algorithms. Mapbox allows customized label modifications with data conditions, but no manual selection or drag-and-drop placement. The ArcGIS Pro documentation 4 states "Label positions are generated automatically.