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Nevertheless, existing well\u2010performing approaches for image similarity measurement only focus on the image itself without utilizing the information of other modalities, while pictures always appear with the described text. Furthermore, those methods need human supervision, yet most images are unlabeled in the real world. Considering the above problems comprehensively, we present a novel visual similarity metric model named PTF\u2010SimCM. It adopts a self\u2010supervised contrastive structure like SimSiam and incorporates a multimodal fusion module to utilize textual modality correlated to the image. We apply a cross\u2010modal model for text modality rather than a standard unimodal text encoder to improve late fusion productivity. In addition, the proposed model employs Sentence PIE\u2010Net to solve the issue caused by polysemous sentences. For simplicity and efficiency, our model learns a specific embedding space where distances directly correspond to the similarity. Experimental results on MSCOCO, Flickr 30k, and Pascal Sentence datasets show that our model overall outperforms all the compared methods in this work, which illustrates that the model can effectively address the issues faced and enhance the performances on unsupervised visual similarity measuring relatively.<\/jats:p>","DOI":"10.1155\/2022\/2343707","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T21:05:07Z","timestamp":1663362307000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PTF\u2010SimCM: A Simple Contrastive Model with Polysemous Text Fusion for Visual Similarity Metric"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9509-0755","authenticated-orcid":false,"given":"Xinpan","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1147-2279","authenticated-orcid":false,"given":"Xinxin","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaojun","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2721-6867","authenticated-orcid":false,"given":"Chengyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"WohlhartP.andLepetitV. 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