{"type":"ScholarlyArticle","title":"CGI: Identifying Conditional Generative Models with Example Images","authors":[{"name":"Zhi Zhou","slug":"zhi-zhou","given":"Zhi","family":"Zhou","affiliation":""},{"name":"Hao-Zhe Tan","slug":"hao-zhe-tan","given":"Hao-Zhe","family":"Tan","affiliation":""},{"name":"Peng-Xiao Song","slug":"peng-xiao-song","given":"Peng-Xiao","family":"Song","affiliation":""},{"name":"Lan-Zhe Guo","slug":"lan-zhe-guo","given":"Lan-Zhe","family":"Guo","affiliation":""}],"abstract":"Generative models have achieved remarkable performance recently, and thus model hubs have emerged. Existing model hubs typically assume basic text matching is sufficient to search for models. However, in reality, due to different abstractions and the large number of models in model hubs, it is not easy for users to review model descriptions and example images, choosing which model best meets their needs. Therefore, it is necessary to describe model functionality wisely so that future users can efficiently search for the most suitable model for their needs. Efforts to address this issue remain limited. In this paper, we propose Conditional Generative Model Identification (CGI), which aims to provide an effective way to identify the most suitable model using user-provided example images rather than requiring users to manually review a large number of models with example images. To address this problem, we propose the Prompt-Based Model Identification (PMI), which can adequately describe model functionality and precisely match requirements with specifications. To evaluate PMI approach and promote related research, we provide a benchmark comprising 65 models and 9100 identification tasks. Extensive experimental and human evaluation results demonstrate that PMI is effective. For instance, 92% of models are correctly identified with significantly better FID scores when four example images are provided.","doi":"10.24963/ijcai.2025/804","url":"https://ijcai.org/proceedings/2025/804","pdf":"https://ijcai.org/proceedings/2025/0804.pdf","markdown":"https://ijcai.org/proceedings/2025/804.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/804","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":804,"pages":{"first":7227,"last":7235},"keywords":["Machine Learning: ML: Learnware/model reuse/transfer learning","Machine Learning: ML: Generative models","Machine Learning: ML: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}