{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/more-cat-than-cute-interpretable-prediction","title":"More cat than cute? Interpretable Prediction of Adjective-Noun Pairs","arxiv_id":"1708.06039","date":"2017-08-21","proceeding":null,"authors":["Delia Fernandez","Alejandro Woodward","Victor Campos","Xavier Giro-i-Nieto","Brendan Jou","Shih-Fu Chang"],"abstract":"The increasing availability of affect-rich multimedia resources has bolstered\ninterest in understanding sentiment and emotions in and from visual content.\nAdjective-noun pairs (ANP) are a popular mid-level semantic construct for\ncapturing affect via visually detectable concepts such as \"cute dog\" or\n\"beautiful landscape\". Current state-of-the-art methods approach ANP prediction\nby considering each of these compound concepts as individual tokens, ignoring\nthe underlying relationships in ANPs. This work aims at disentangling the\ncontributions of the `adjectives' and `nouns' in the visual prediction of ANPs.\nTwo specialised classifiers, one trained for detecting adjectives and another\nfor nouns, are fused to predict 553 different ANPs. The resulting ANP\nprediction model is more interpretable as it allows us to study contributions\nof the adjective and noun components. Source code and models are available at\nhttps://imatge-upc.github.io/affective-2017-musa2/ .","url_abs":"http://arxiv.org/abs/1708.06039v1","url_pdf":"http://arxiv.org/pdf/1708.06039v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"more-cat-than-cute-interpretable-prediction","repo_url":"https://github.com/imatge-upc/affective-2017-musa2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}