Monahan, T. & Wood, D. M. Surveillance Studies: A Reader (Oxford Univ. Press, 2018).
Lyon, D. in Emerging Digital Spaces in Contemporary Society: Properties of Technology (eds Kalantzis-Cope, P. & Gherab-Martin, K.) 107–120 (Springer, 2010).
Scheuerman, M. K., Hanna, A. & Denton, E. Do datasets have politics? Disciplinary values in computer vision dataset development. Proc. ACM Hum.–Comput. Interact. 5, 317 (2021).
Browne, S. Dark Matters: On the Surveillance of Blackness (Duke Univ. Press, 2015).
Agre, P. E. Surveillance and capture: Two models of privacy. Inf. Soc. 10, 101–127 (1994).
Stark, L. Facial recognition is the plutonium of AI. XRDS: Crossroads 25, 50–55 (2019).
Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (Profile Books, 2019).
Building community power to abolish the police state. Stop LAPD Spying Coalition https://stoplapdspying.org/ (accessed 1 September 2023).
Chang, M. et al. Countermeasures: The Need for New Legislation to Govern Biometric Technologies in the UK (2022).
Marx, G. T. in International Encyclopedia of the Social & Behavioral Sciences 2nd edn (ed. Wright, J. D.) 733–741 (Elsevier, 2015).
Monahan, T. & Murakami Wood, D. Introduction: Surveillance Studies as a Transdisciplinary Endeavor (2018).
Foucault, M. Discipline and Punish: The Birth of the Prison (Pantheon Books, 1977).
Deleuze, G. Postscript on the Societies of Control (MIT Press, 1992).
Allmer, T. Critical surveillance studies in the information society. tripleC: Commun. Capitalism Crit. 9, 566–592 (2011).
Richards, N. M. The dangers of surveillance. Harv. Law Rev. 126, 1934 (2013).
Dobson, J. E. The Birth of Computer Vision (Univ. Minnesota Press, 2023).
Raji, I. D. & Fried, G. About face: a survey of facial recognition evaluation. Preprint at https://arxiv.org/abs/2102.00813 (2021).
Broussard, M. Artificial Unintelligence: How Computers Misunderstand the World (MIT Press, 2018).
Königs, P. Government surveillance, privacy, and legitimacy. Philos. Technol. 35, 8 (2022).
Szeliski, R Computer Vision: Algorithms and Applications (Springer, 2020).
Forsyth, D. & Ponce, J. Computer Vision: A Modern Approach (Pearson, 2011).
Call for papers. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR 2024); https://cvpr.thecvf.com/Conferences/2024/CallForPapers.
Keynotes and panels. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR 2024); https://cvpr.thecvf.com/Conferences/2024/KeynotesAndPanels.
Zitnick, L. et al. Spherical channels for modeling atomic interactions. In Proc. 35th Conference on Neural Information Processing Systems (eds Koyejo, S. et al.) 8054–8067 (Curran Associates, 2022).
Hill, K. Your Face Belongs to Us: The Secretive Startup Dismantling Your Privacy (Simon and Schuster, 2023).
Watt, E. The right to privacy and the future of mass surveillance. Int. J. Hum. Rights 21, 773–799 (2017).
Pridmore, J. & Zwick, D. Marketing and the rise of commercial consumer surveillance. Surveill. Soc. 8, 269–277 (2011).
Andrejevic, M. in Routledge Handbook of Surveillance Studies (eds Ball, K. et al.) 91–98 (Routledge, 2012).
Zuboff, S. in Social Theory Re-wired (eds Longhofer, W. & Winchester, D.) 203–213 (Routledge, 2023).
Csernatoni, R. & Lavallée, C. in Emerging Security Technologies and EU Governance (eds Calcara, A. et al.) 206–223 (Routledge, 2020).
Almansoori, M., Gallardo, A., Poveda, J., Ahmed, A. & Chatterjee, R. A global survey of Android dual-use applications used in intimate partner surveillance. In Proc. on Privacy Enhancing Technologies (eds Kerschbaum, F. & Mazurek, M. L.) 120–139 (Privacy Enhancing Technologies Board, 2022).
Selinger, E. & Durant, D. Amazon’s ring: surveillance as a slippery slope service. Sci. Cult. 31, 92–106 (2022).
Nesterova, I. Questioning the EU proposal for an artificial intelligence act: the need for prohibitions and a stricter approach to biometric surveillance. Inf. Polity 27, 147–162 (2022).
Kalluri, P. R. & Agnew, W. Code and data for ‘Computer vision research powers surveillance technology’. Github https://github.com/wagnew3/Computer-Vision-Research-Powers-Surveillance-Technology (2025).
The fight to stop face recognition technology. American Civil Liberties Union www.aclu.org/news/topic/stopping-face-recognition-surveillance (accessed 1 September 2023).
Awumey, E., Das, S. & Forlizzi, J. A systematic review of biometric monitoring in the workplace: analyzing socio-technical harms in development, deployment and use. In Proc. 2024 ACM Conference on Fairness, Accountability, and Transparency 920–932 (2024).
Murray, D. et al. The chilling effects of surveillance and human rights: insights from qualitative research in Uganda and Zimbabwe. J. Hum. Rights Pract. 16, 397–412 (2023).
Cohen, J. E. in Cambridge Handbook of Surveillance Law (eds Gray, D. & Henderson, S. E.) 455–469 (Cambridge Univ. Press, 2017).
Monroe, B. L., Colaresi, M. P. & Quinn, K. M. Fightin’ words: Lexical feature selection and evaluation for identifying the content of political conflict. Polit. Anal. 16, 372–403 (2008).
Ahmed, N. & Wahed, M. The de-democratization of AI: deep learning and the compute divide in artificial intelligence research. Preprint at https://arxiv.org/abs/2010.15581 (2020).
Leslie, S. W. et al. The Cold War and American Science: The Military-Industrial-Academic Complex at MIT and Stanford (Columbia Univ. Press, 1993).
Feldstein, S. The Global Expansion of AI Surveillance (Carnegie Endowment for International Peace, 2019).
Carrier, J. G. Misrecognition and knowledge. Inquiry 22, 321–342 (1979).
Véliz, C. Privacy Is Power (Melville House, 2021).
Waelen, R. A. The ethics of computer vision: an overview in terms of power. AI Ethics 4, 353–362 (2024).
Haraway, D. in Feminist Theory Reader (eds McCann, C. et al.) 303–310 (Routledge, 2020).
Ensmenger, N. L. The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise (MIT Press, 2012).
Birhane, A. et al. The values encoded in machine learning research. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 173–184 (ACM, 2022).
Agre, P. E. in Social Science, Technical Systems and Cooperative Work: Beyond the Great Divide (eds Bowker, G. et al.) Ch. 6 (Psychology Press, 1997).
Butcher, S. I. Origins of the Russell–Einstein manifesto. Technical report (Pugwash Conferences on Science and World Affairs, 2005).
Cevikalp, H. & Triggs, B. Face recognition based on image sets. In Proc. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2567–2573 (IEEE, 2010).
Salakhutdinov, R., Torralba, A. & Tenenbaum, J. Learning to share visual appearance for multiclass object detection. In Proc. CVPR 2011 1481–1488 (IEEE, 2011).
Khamis, S., Morariu, V. I. & Davis, L. S. A flow modelfor joint action recognition and identity maintenance. In Proc. 2012 IEEE Conference on Computer Vision and Pattern Recognition 1218–1225 (IEEE, 2012).
Chen, C.-Y. & Grauman, K. Watching unlabeled video helpslearn new human actions from very few labeled snapshots. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 572–579 (IEEE, 2013).
Lin, G., Shen, C., Shi, Q., Van den Hengel, A. & Suter, D. Fast supervised hashing with decision trees for high-dimensional data. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1963–1970 (IEEE, 2014).
Yim, J. et al. Rotating your face using multi-task deep neural network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 676–684 (IEEE, 2015).
Song, S. et al. Multimodal multi-stream deeplearning for egocentric activity recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops 24–31 (IEEE, 2016).
Arvanitopoulos, N., Achanta, R. & Susstrunk, S. Single image reflection suppression. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4498–4506 (IEEE, 2017).
Bloesch, M., Czarnowski, J., Clark, R., Leutenegger, S. & Davison, A. J. Codeslam—learning a compact, optimisable representationfor dense visual slam. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2560–2568 (IEEE, 2018).
Shi, L., Zhang, Y., Cheng, J. & Lu, H. Skeleton-based action recognition with directed graph neural networks. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 7912–7921 (IEEE, 2019).
Wang, W., Wang, Y., Huang, Q. & Gao, W. Measuring visual saliency by site entropy rate. In Proc. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2368–2375 (IEEE, 2010).
Zhang, Y., Jia, Z. & Chen, T. Image retrieval with geometry-preserving visual phrases. In Proc. CVPR 2011 809–816 (IEEE, 2011).
Ranjbar, M., Vahdat, A. & Mori, G. Complexloss optimization via dual decomposition. In Proc. 2012 IEEE Conference on Computer Vision and Pattern Recognition 2304–2311 (IEEE, 2012).
Fidler, S., Sharma, A. & Urtasun, R. A sentence is worth a thousand pixels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1995–2002 (IEEE, 2013).
Bae, S.-H. & Yoon, K.-J. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1218–1225 (IEEE, 2014).
Zhao, R., Ouyang, W., Li, H. & Wang, X. Saliency detection by multi-context deep learning. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1265–1274 (IEEE, 2015).
Wang, L., Qiao, Y., Tang, X. & Van Gool, L. Actionness estimation using hybrid fully convolutional networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2708–2717 (IEEE, 2016).
Liu, W. et al. Sphereface: deep hypersphere embedding for face recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 212–220 (IEEE, 2017).
Wan, F., Wei, P., Jiao, J., Han, Z. & Ye, Q. Min-entropy latent model for weakly supervised object detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1297–1306 (IEEE, 2018).
Ranjan, A. et al. Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 12240–12249 (IEEE, 2019).
Socher, R. & Fei-Fei, L. Connecting modalities: semi-supervised segmentation and annotation of images using unaligned text corpora. In Proc. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 966–973 (IEEE, 2010).
Harker, M. & O’Leary, P. Least squares surface reconstruction from gradients: direct algebraic methods with spectral, Tikhonov, and constrained regularization. In Proc. CVPR 2011 2529–2536 (IEEE, 2011).
He, J., Balzano, L. & Szlam, A. Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video. In Proc. 2012 IEEE Conference on Computer Vision and Pattern Recognition 1568–1575 (IEEE, 2012).
Khosla, A., Hamid, R., Lin, C.-J. & Sundaresan, N. Large-scale video summarization using web-image priors. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2698–2705 (IEEE, 2013).
Tang, K., Yang, J. & Wang, J. Investigating haze-relevant features in a learning framework for image dehazing. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2995–3000 (IEEE, 2014).
Chen, X., Ma, H., Wang, X. & Zhao, Z. Improving object proposals with multi-thresholding straddling expansion. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2587–2595 (IEEE, 2015).
Hu, S. et al. A polarimetric thermal database for face recognition research. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops 119–126 (IEEE, 2016).
Dansereau, D. G., Eriksson, A. & Leitner, J. Richardson-Lucy deblurring for moving light field cameras. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops 70–81 (IEEE, 2017).
Hu, R., Dollár, P., He, K., Darrell, T. & Girshick, R. Learning to segment everything. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4233–4241 (IEEE, 2018).
Acuna, D., Kar, A. & Fidler, S. Devil is in the edges: learning semantic boundaries from noisy annotations. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 11075–11083 (IEEE, 2019) .
Wu, Y., Shen, B. & Ling, H. Online robust image alignment via iterative convex optimization. In Proc. 2012 IEEE Conference on Computer Vision and Pattern Recognition 1808–1814 (IEEE, 2012).
GengtÃ, B., Yang, L., Xu, C. & Hua, X.-S. Content-aware ranking for visual search. In Proc. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 3400–3407 (IEEE, 2010).
Angelova, A. & Zhu, S. Efficient object detection and segmentation for fine-grained recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 811–818 (IEEE, 2013).
Lin, J., Liu. Y., Hullin, M. B. & Dai, Q. Fourier analysis on transient imaging with a multifrequency time-of-flight camera. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 3230–3237 (IEEE, 2014).
Bernard, F. et al. A solution for multi-alignment by transformation synchronisation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2161–2169 (IEEE, 2015).
Yu, H., Wang, J., Huang, Z., Yang, Y. & Xu, W. Video paragraph captioning using hierarchical recurrent neural networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4584–4593 (IEEE, 2016).
Zhang, H., Kyaw, Z., Chang, S.-F. & Chua. T.-S. Visual translation embedding network for visual relation detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 5532–5540 (IEEE, 2017).
Volpi, R., Morerio, P., Savarese, S. & Murino, V. Adversarial feature augmentation for unsupervised domain adaptation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 5495–5504 (IEEE, 2018).
Wu, D., Dai, Q., Liu, J., Li, B. & Wang, W. Deep incremental hashing networkfor efficient image retrieval. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9069–9077 (IEEE, 2019).
Vijayanarasimhan, S. & Kapoor, A. Visual recognition and detection under bounded computational resources. In Proc. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1006–1013 (IEEE, 2010).
Balzer, J., Hofer, S. & Beyerer, J. Multiview specular stereo reconstruction of large mirror surfaces. In Proc. CVPR 2011 2537–2544 (IEEE, 2011).
Saberian, M. J. & Vasconcelos, N. Boosting algorithms for simultaneous feature extraction and selection. In Proc. 2012 IEEE Conference on Computer Visionand Pattern Recognition 2448–2455 (IEEE, 2012).
Zhou, Z., Jin, H. & Ma, Y. Plane-based content preserving warps for video stabilization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2299–2306 (IEEE, 2013).
Danelljan, M., Khan, F. S., Felsberg, M. & Van de Weijer, J. Adaptive color attributes for real-time visual tracking. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1090–1097 (IEEE, 2014).
Moreno, D., Son, K. & Taubin, G. Embedded phase shifting: robust phase shifting with embedded signals. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2301–2309 (IEEE, 2015).
Hu, R. et al. Natural language object retrieval. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4555–4564 (IEEE, 2016).
Fanello, S. R et al. Ultrastereo: efficient learning-based matching for active stereo systems. In Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 6535–6544 (IEEE, 2017).
Nguyen, P., Liu, T., Prasad, G. & Han, B. Weakly supervised action localization by sparse temporal pooling network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 6752–6761 (IEEE, 2018).
Zhang, J. & Peng, Y. Object-aware aggregation with bidirectional temporal graph for video captioning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 8327–8336 (IEEE, 2019).
Raheja, J. L., Das, K. & Chaudhary, A. An efficient real time method of fingertip detection. In Proc. 7th International Conference on Trends in Industrial Measurements and Automation 447–450 (TIMA, 2011).
Hammarfelt, B. Linking science to technology: the ‘patent paper citation’ and the rise of patentometrics in the 1980s. J. Doc. 77, 1413–1429 (2021).
Ahmadpoor, M. & Jones, B. F. The dual frontier: patented inventions and prior scientific advance. Science 357, 583–587 (2017).
IEEE Computer Society Team. CVPR 2021 Report Identifies 5 Trend Areas (IEEE, 2021).
Sinha, A. et al. An overview of Microsoft Academic Service (MAS) and applications. In Proc. 24th International Conference on World Wide Web 243–246 (2015).
Marx, M. & Fuegi, A. Reliance on science by inventors: hybrid extraction of in-text patent-to-article citations. J. Econ. Manag. Strategy 31, 369–392 (2022).
Finardi, U. Time relations between scientific production and patenting of knowledge: the case of nanotechnologies. Scientometrics 89, 37–50 (2011).
Abdalla, M. & Abdalla, M. The grey hoodie project: big tobacco, big tech, and the threat on academic integrity. In Proc. 2021 AAAI/ACM Conference on AI, Ethics, and Society 287–297 (2021).