{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:51:54Z","timestamp":1775069514804,"version":"3.50.1"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"ETRA","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Cluster of Excellence - Machine Learning","award":["EXC number 2064\/1 - Project number 390727645."],"award-info":[{"award-number":["EXC number 2064\/1 - Project number 390727645."]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:p>Human drivers use their attentional mechanisms to focus on critical objects and make decisions while driving. As human attention can be revealed from gaze data, capturing and analyzing gaze information has emerged in recent years to benefit autonomous driving technology. Previous works in this context have primarily aimed at predicting \"where\" human drivers look at and lack knowledge of \"what\" objects drivers focus on. Our work bridges the gap between pixel-level and object-level attention prediction. Specifically, we propose to integrate an attention prediction module into a pretrained object detection framework and predict the attention in a grid-based style. Furthermore, critical objects are recognized based on predicted attended-to areas. We evaluate our proposed method on two driver attention datasets, BDD-A and DR(eye)VE. Our framework achieves competitive state-of-the-art performance in the attention prediction on both pixel-level and object-level but is far more efficient (75.3 GFLOPs less) in computation.<\/jats:p>","DOI":"10.1145\/3530887","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T22:17:43Z","timestamp":1652480263000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Where and What"],"prefix":"10.1145","volume":"6","author":[{"given":"Yao","family":"Rong","sequence":"first","affiliation":[{"name":"University of T\u00fcbingen, T\u00fcbingen, Germany"}]},{"given":"Naemi-Rebecca","family":"Kassautzki","sequence":"additional","affiliation":[{"name":"University of T\u00fcbingen, T\u00fcbingen, Germany"}]},{"given":"Wolfgang","family":"Fuhl","sequence":"additional","affiliation":[{"name":"University of T\u00fcbingen, T\u00fcbingen, Germany"}]},{"given":"Enkelejda","family":"Kasneci","sequence":"additional","affiliation":[{"name":"University of T\u00fcbingen, T\u00fcbingen, Germany"}]}],"member":"320","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving. arXiv preprint arXiv:2002.11020","author":"Aksoy Ekrem","year":"2020","unstructured":"Ekrem Aksoy , Ahmet Yazici , and Mahmut Kasap . 2020. See , Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving. arXiv preprint arXiv:2002.11020 ( 2020 ). Ekrem Aksoy, Ahmet Yazici, and Mahmut Kasap. 2020. See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving. arXiv preprint arXiv:2002.11020 (2020)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Stefano Alletto Andrea Palazzi Francesco Solera Simone Calderara and Rita Cucchiara. 2016. Dr (eye) ve: a dataset for attention-based tasks with applications to autonomous and assisted driving. In CVPRW .  Stefano Alletto Andrea Palazzi Francesco Solera Simone Calderara and Rita Cucchiara. 2016. Dr (eye) ve: a dataset for attention-based tasks with applications to autonomous and assisted driving. In CVPRW .","DOI":"10.1109\/CVPRW.2016.14"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Michael Barz Sebastian Kapp Jochen Kuhn and Daniel Sonntag. 2021. 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