{"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/interpretable-counting-for-visual-question","title":"Interpretable Counting for Visual Question Answering","arxiv_id":"1712.08697","date":"2017-12-23","proceeding":"ICLR 2018 1","authors":["Alexander Trott","Caiming Xiong","Richard Socher"],"abstract":"Questions that require counting a variety of objects in images remain a major\nchallenge in visual question answering (VQA). The most common approaches to VQA\ninvolve either classifying answers based on fixed length representations of\nboth the image and question or summing fractional counts estimated from each\nsection of the image. In contrast, we treat counting as a sequential decision\nprocess and force our model to make discrete choices of what to count.\nSpecifically, the model sequentially selects from detected objects and learns\ninteractions between objects that influence subsequent selections. A\ndistinction of our approach is its intuitive and interpretable output, as\ndiscrete counts are automatically grounded in the image. Furthermore, our\nmethod outperforms the state of the art architecture for VQA on multiple\nmetrics that evaluate counting.","url_abs":"http://arxiv.org/abs/1712.08697v2","url_pdf":"http://arxiv.org/pdf/1712.08697v2.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":[],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[{"slug":"howmany-qa","name":"HowMany-QA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.08697","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}