{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:19:30Z","timestamp":1773512370190,"version":"3.50.1"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T00:00:00Z","timestamp":1624406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Optilight, 14671","award":["STW (project number: 10022739) --- Stichting voor de Technische Wetenschappen"],"award-info":[{"award-number":["STW (project number: 10022739) --- Stichting voor de Technische Wetenschappen"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,6,23]]},"abstract":"<jats:p>High-quality lighting positively influences visual performance in humans. The experienced visual performance can be measured using desktop luminance and hence several lighting control systems have been developed for its quantification. However, the measurement devices that are used to monitor the desktop luminance in existing lighting control systems are obtrusive to the users. As an alternative, ceiling-based luminance projection sensors are being used recently as these are unobtrusive and can capture the direct task area of a user. The positioning of these devices on the ceiling requires to estimate the desktop luminance in the user's vertical visual field, solely using ceiling-based measurements, to better predict the experienced visual performance of the user. For this purpose, we present LUMNET, an approach for estimating desktop luminance with deep models through utilizing supervised and self-supervised learning. Our model learns visual representations from ceiling-based images, which are collected in indoor spaces within the physical vicinity of the user to predict average desktop luminance as experienced in a real-life setting. We also propose a self-supervised contrastive method for pre-training LUMNET with unlabeled data and we demonstrate that the learned features are transferable onto a small labeled dataset which minimizes the requirement of costly data annotations. Likewise, we perform experiments on domain-specific datasets and show that our approach significantly improves over the baseline results from prior methods in estimating luminance, particularly in the low-data regime. LUMNET is an important step towards learning-based technique for luminance estimation and can be used for adaptive lighting control directly on-device thanks to its minimal computational footprint with an added benefit of preserving user's privacy.<\/jats:p>","DOI":"10.1145\/3463500","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T16:29:19Z","timestamp":1624552159000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["LumNet"],"prefix":"10.1145","volume":"5","author":[{"given":"Prince U.C.","family":"Songwa","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaqib","family":"Saeed","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sachin","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thijs W.","family":"Kruisselbrink","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanir","family":"Ozcelebi","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Philip Bachman R Devon Hjelm and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. In Advances in Neural Information Processing Systems. 15535--15545.  Philip Bachman R Devon Hjelm and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. In Advances in Neural Information Processing Systems. 15535--15545."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/074873049501000209"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/096032717300500301"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/1477153506070994"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"e_1_2_1_6_1","volume-title":"A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020. A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 ( 2020 ). Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)."},{"key":"e_1_2_1_7_1","volume-title":"On denoising autoencoders trained to minimise binary cross-entropy. arXiv preprint arXiv:1708.08487","author":"Creswell Antonia","year":"2017","unstructured":"Antonia Creswell , Kai Arulkumaran , and Anil A Bharath . 2017. On denoising autoencoders trained to minimise binary cross-entropy. arXiv preprint arXiv:1708.08487 ( 2017 ). Antonia Creswell, Kai Arulkumaran, and Anil A Bharath. 2017. On denoising autoencoders trained to minimise binary cross-entropy. arXiv preprint arXiv:1708.08487 (2017)."},{"key":"e_1_2_1_8_1","volume-title":"Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501","author":"Cubuk Ekin D","year":"2018","unstructured":"Ekin D Cubuk , Barret Zoph , Dandelion Mane , Vijay Vasudevan , and Quoc V Le . 2018 . Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018). Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. 2018. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1191\/1365782804li115oa"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_11_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_2_1_12_1","volume-title":"12th European Lighting Conference.","author":"Doulos LT","year":"2013","unstructured":"LT Doulos , A Tsangrassoulis , CA Bouroussis , and FV Topalis . 2013 . Reviewing drawbacks of conventional photosensors: are CCD\/CMOS sensors the next generation?. In Lux Europa 2013 , 12th European Lighting Conference. LT Doulos, A Tsangrassoulis, CA Bouroussis, and FV Topalis. 2013. Reviewing drawbacks of conventional photosensors: are CCD\/CMOS sensors the next generation?. In Lux Europa 2013, 12th European Lighting Conference."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2009.01.002"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00630"},{"key":"e_1_2_1_15_1","volume-title":"Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728","author":"Gidaris Spyros","year":"2018","unstructured":"Spyros Gidaris , Praveer Singh , and Nikos Komodakis . 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 ( 2018 ). Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1191\/1365782806li164oa"},{"key":"e_1_2_1_19_1","first-page":"1","article-title":"The CIE XYZ and xyY color spaces","volume":"1","author":"Kerr Douglas A","year":"2010","unstructured":"Douglas A Kerr . 2010 . The CIE XYZ and xyY color spaces . Colorimetry 1 , 1 (2010), 1 -- 16 . Douglas A Kerr. 2010. The CIE XYZ and xyY color spaces. Colorimetry 1, 1 (2010), 1--16.","journal-title":"Colorimetry"},{"key":"e_1_2_1_20_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_1_21_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_2_1_22_1","unstructured":"Ryan Kiros Yukun Zhu Russ R Salakhutdinov Richard Zemel Raquel Urtasun Antonio Torralba and Sanja Fidler. 2015. Skip-thought vectors. In Advances in neural information processing systems. 3294--3302.  Ryan Kiros Yukun Zhu Russ R Salakhutdinov Richard Zemel Raquel Urtasun Antonio Torralba and Sanja Fidler. 2015. Skip-thought vectors. In Advances in neural information processing systems. 3294--3302."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00202"},{"key":"e_1_2_1_24_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.  Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.26607\/ijsl.v19i1.76"},{"key":"e_1_2_1_27_1","volume-title":"Ceiling-based luminance measurements: a feasible solution?","author":"Kruisselbrink Thijs","year":"2019","unstructured":"Thijs Kruisselbrink , Rajendra Dangol , and Evert van Loenen . 2019. Ceiling-based luminance measurements: a feasible solution? ( 2019 ). Thijs Kruisselbrink, Rajendra Dangol, and Evert van Loenen. 2019. Ceiling-based luminance measurements: a feasible solution? (2019)."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2020.106699"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1177\/096032719302500203"},{"key":"e_1_2_1_30_1","volume-title":"Backpropagation applied to handwritten zip code recognition. Neural computation 1, 4","author":"LeCun Yann","year":"1989","unstructured":"Yann LeCun , Bernhard Boser , John S Denker , Donnie Henderson , Richard E Howard , Wayne Hubbard , and Lawrence D Jackel . 1989. Backpropagation applied to handwritten zip code recognition. Neural computation 1, 4 ( 1989 ), 541--551. Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation 1, 4 (1989), 541--551."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.472"},{"key":"e_1_2_1_32_1","unstructured":"Jiasen Lu Caiming Xiong Devi Parikh and Richard Socher. 2017. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning. arXiv:cs.CV\/1612.01887  Jiasen Lu Caiming Xiong Devi Parikh and Richard Socher. 2017. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning. arXiv:cs.CV\/1612.01887"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.877518"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski etal 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529--533.  Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529--533.","DOI":"10.1038\/nature14236"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1191\/1365782802lt047oa"},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale. LESO-PB, EPFL, 235--240","author":"Motamed Ali","year":"2015","unstructured":"Ali Motamed , Laurent Deschamps , and Jean-louis Scartezzini. 2015 . Validation and preliminary experiments of embedded discomfort glare assessment through a novel HDR vision sensor . In Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale. LESO-PB, EPFL, 235--240 . Ali Motamed, Laurent Deschamps, and Jean-louis Scartezzini. 2015. Validation and preliminary experiments of embedded discomfort glare assessment through a novel HDR vision sensor. In Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale. LESO-PB, EPFL, 235--240."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.05.017"},{"key":"e_1_2_1_38_1","volume-title":"Asian conference on computer vision. Springer, 709--720","author":"Nguyen Hieu V","year":"2010","unstructured":"Hieu V Nguyen and Li Bai . 2010 . Cosine similarity metric learning for face verification . In Asian conference on computer vision. Springer, 709--720 . Hieu V Nguyen and Li Bai. 2010. Cosine similarity metric learning for face verification. In Asian conference on computer vision. Springer, 709--720."},{"key":"e_1_2_1_39_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 ( 2018 ). Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_2_1_40_1","volume-title":"Contrastive learning for unpaired image-to-image translation. arXiv preprint arXiv:2007.15651","author":"Park Taesung","year":"2020","unstructured":"Taesung Park , Alexei A Efros , Richard Zhang , and Jun-Yan Zhu . 2020. Contrastive learning for unpaired image-to-image translation. arXiv preprint arXiv:2007.15651 ( 2020 ). Taesung Park, Alexei A Efros, Richard Zhang, and Jun-Yan Zhu. 2020. Contrastive learning for unpaired image-to-image translation. arXiv preprint arXiv:2007.15651 (2020)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0327(99)00063-4"},{"key":"e_1_2_1_42_1","volume-title":"Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462","author":"Patro S","year":"2015","unstructured":"S Patro and Kishore Kumar Sahu . 2015 . Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462 (2015). S Patro and Kishore Kumar Sahu. 2015. Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462 (2015)."},{"key":"e_1_2_1_43_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford Alec","year":"2015","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)."},{"key":"e_1_2_1_44_1","volume-title":"Ng","author":"Rajpurkar Pranav","year":"2017","unstructured":"Pranav Rajpurkar , Jeremy Irvin , Kaylie Zhu , Brandon Yang , Hershel Mehta , Tony Duan , Daisy Ding , Aarti Bagul , Curtis Langlotz , Katie Shpanskaya , Matthew P. Lungren , and Andrew Y . Ng . 2017 . CheXNet: Radiologist- Level Pneumonia Detection on Chest X-Rays with Deep Learning . arXiv:cs.CV\/1711.05225 Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, and Andrew Y. Ng. 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv:cs.CV\/1711.05225"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1177\/096032719102300301"},{"key":"e_1_2_1_46_1","unstructured":"Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems. 91--99.  Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems. 91--99."},{"key":"e_1_2_1_47_1","volume-title":"Sense and Learn: Self-Supervision for Omnipresent Sensors. arXiv preprint arXiv:2009.13233","author":"Saeed Aaqib","year":"2020","unstructured":"Aaqib Saeed , Victor Ungureanu , and Beat Gfeller . 2020. Sense and Learn: Self-Supervision for Omnipresent Sensors. arXiv preprint arXiv:2009.13233 ( 2020 ). Aaqib Saeed, Victor Ungureanu, and Beat Gfeller. 2020. Sense and Learn: Self-Supervision for Omnipresent Sensors. arXiv preprint arXiv:2009.13233 (2020)."},{"key":"e_1_2_1_48_1","volume-title":"Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229","author":"Sermanet Pierre","year":"2013","unstructured":"Pierre Sermanet , David Eigen , Xiang Zhang , Micha\u00ebl Mathieu , Rob Fergus , and Yann LeCun . 2013 . Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013). Pierre Sermanet, David Eigen, Xiang Zhang, Micha\u00ebl Mathieu, Rob Fergus, and Yann LeCun. 2013. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8462891"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.131"},{"key":"e_1_2_1_51_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_2_1_52_1","volume-title":"Visual fields. Clinical methods: The history, physical, and laboratory examinations","author":"Spector Robert H","year":"1990","unstructured":"Robert H Spector . 1990. Visual fields. Clinical methods: The history, physical, and laboratory examinations ( 1990 ). Robert H Spector. 1990. Visual fields. Clinical methods: The history, physical, and laboratory examinations (1990)."},{"key":"e_1_2_1_53_1","volume-title":"CURL: Contrastive Unsupervised Representations for Reinforcement Learning. arXiv:cs.LG\/2004.04136","author":"Srinivas Aravind","year":"2020","unstructured":"Aravind Srinivas , Michael Laskin , and Pieter Abbeel . 2020 . CURL: Contrastive Unsupervised Representations for Reinforcement Learning. arXiv:cs.LG\/2004.04136 Aravind Srinivas, Michael Laskin, and Pieter Abbeel. 2020. CURL: Contrastive Unsupervised Representations for Reinforcement Learning. arXiv:cs.LG\/2004.04136"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"e_1_2_1_55_1","volume-title":"Lighting for work: a review of visual and biological effects. Lighting research & technology 36, 4","author":"Van Bommel WJM","year":"2004","unstructured":"WJM Van Bommel and GJ Van den Beld . 2004. Lighting for work: a review of visual and biological effects. Lighting research & technology 36, 4 ( 2004 ), 255--266. WJM Van Bommel and GJ Van den Beld. 2004. Lighting for work: a review of visual and biological effects. Lighting research & technology 36, 4 (2004), 255--266."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1080\/15502724.2014.881720"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1177\/096032710003200404"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1177\/1477153507086279"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.320"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"e_1_2_1_62_1","unstructured":"Greg Ward. [n.d.]. Anyhere Software. http:\/\/www.anyhere.com\/  Greg Ward. [n.d.]. Anyhere Software. http:\/\/www.anyhere.com\/"},{"key":"e_1_2_1_63_1","unstructured":"Zhirong Wu Yuanjun Xiong Stella Yu and Dahua Lin. 2018. Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination. arXiv:cs.CV\/1805.01978  Zhirong Wu Yuanjun Xiong Stella Yu and Dahua Lin. 2018. Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination. arXiv:cs.CV\/1805.01978"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.76"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3463500","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3463500","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:28Z","timestamp":1750195888000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3463500"}},"subtitle":["Learning to Estimate Vertical Visual Field Luminance for Adaptive Lighting Control"],"short-title":[],"issued":{"date-parts":[[2021,6,23]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6,23]]}},"alternative-id":["10.1145\/3463500"],"URL":"https:\/\/doi.org\/10.1145\/3463500","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,23]]},"assertion":[{"value":"2021-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}