{"isi":1,"doi":"10.1109/ICCVW.2019.00217","conference":{"end_date":"2019-10-28","location":"Seoul, South Korea","name":"ICCVW: International Conference on Computer Vision Workshop","start_date":"2019-10-27"},"year":"2019","type":"conference","month":"10","citation":{"short":"A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.","ama":"Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships using box attention. In: <i>Proceedings of the 2019 International Conference on Computer Vision Workshop</i>. IEEE; 2019. doi:<a href=\"https://doi.org/10.1109/ICCVW.2019.00217\">10.1109/ICCVW.2019.00217</a>","chicago":"Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio Ferrari. “Detecting Visual Relationships Using Box Attention.” In <i>Proceedings of the 2019 International Conference on Computer Vision Workshop</i>. IEEE, 2019. <a href=\"https://doi.org/10.1109/ICCVW.2019.00217\">https://doi.org/10.1109/ICCVW.2019.00217</a>.","ieee":"A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual relationships using box attention,” in <i>Proceedings of the 2019 International Conference on Computer Vision Workshop</i>, Seoul, South Korea, 2019.","ista":"Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual relationships using box attention. Proceedings of the 2019 International Conference on Computer Vision Workshop. ICCVW: International Conference on Computer Vision Workshop, 1749–1753.","mla":"Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.” <i>Proceedings of the 2019 International Conference on Computer Vision Workshop</i>, 1749–1753, IEEE, 2019, doi:<a href=\"https://doi.org/10.1109/ICCVW.2019.00217\">10.1109/ICCVW.2019.00217</a>.","apa":"Kolesnikov, A., Kuznetsova, A., Lampert, C., &#38; Ferrari, V. (2019). Detecting visual relationships using box attention. In <i>Proceedings of the 2019 International Conference on Computer Vision Workshop</i>. Seoul, South Korea: IEEE. <a href=\"https://doi.org/10.1109/ICCVW.2019.00217\">https://doi.org/10.1109/ICCVW.2019.00217</a>"},"publication_status":"published","department":[{"_id":"ChLa"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1807.02136"}],"oa_version":"Preprint","project":[{"name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036"}],"external_id":{"arxiv":["1807.02136"],"isi":["000554591601098"]},"date_updated":"2023-09-08T11:18:37Z","article_number":"1749-1753","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","ec_funded":1,"publisher":"IEEE","status":"public","date_published":"2019-10-01T00:00:00Z","publication_identifier":{"isbn":["9781728150239"]},"author":[{"last_name":"Kolesnikov","first_name":"Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87","full_name":"Kolesnikov, Alexander"},{"full_name":"Kuznetsova, Alina","first_name":"Alina","last_name":"Kuznetsova"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887"},{"first_name":"Vittorio","last_name":"Ferrari","full_name":"Ferrari, Vittorio"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"oa":1,"title":"Detecting visual relationships using box attention","_id":"7640","date_created":"2020-04-05T22:00:51Z","scopus_import":"1","publication":"Proceedings of the 2019 International Conference on Computer Vision Workshop","abstract":[{"lang":"eng","text":"We propose a new model for detecting visual relationships, such as \"person riding motorcycle\" or \"bottle on table\". This task is an important step towards comprehensive structured mage understanding, going beyond detecting individual objects. Our main novelty is a Box Attention mechanism that allows to model pairwise interactions between objects using standard object detection pipelines. The resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures. Moreover, unlike previously proposed approaches, our model does not introduce any additional complex components or hyperparameters on top of those already required by the underlying detection model. We conduct an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating strong quantitative and qualitative results."}],"quality_controlled":"1","day":"01"}