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Incorporating physics into data-driven computer vision

Nature Machine Intelligence volume 5, pages 572–580 (2023)Cite this article

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Abstract

Many computer vision techniques infer properties of our physical world from images. Although images are formed through the physics of light and mechanics, computer vision techniques are typically data driven. This trend is mostly performance related: classical techniques from physics-based vision often score lower on metrics compared with modern deep learning. However, recent research, covered in this Perspective, has shown that physical models can be included as a constraint into data-driven pipelines. In doing so, one can combine the performance benefits of a data-driven method with advantages offered from a physics-based method, such as intepretability, falsifiability and generalizability. The aim of this Perspective is to provide an overview into specific approaches for integrating physical models into artificial intelligence pipelines, referred to as physics-based machine learning. We discuss technical approaches that range from modifications to the dataset, network design, loss functions, optimization and regularization schemes.

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Fig. 1: Incorporating physics in neural pipelines in modern computer vision.
Fig. 2: When to approach a problem from a physics, data-driven or physics-based learning approach.
Fig. 3: Two techniques to incorporate physics into machine learning pipelines.
Fig. 4: Combined loss functions that use both data-driven annotations and physical constraints.

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Acknowledgements

The authors thank members of the Visual Machines Group for feedback and support, as well as P. Patwa, Y. Ba, H. Peters, A. Armouti, H. Zhang, E. Zhao, S. Zhou, S. Vilesov, P. Chari, Z. Wang, A. Gupta, D. Conover, A. Singh and A. Wong for technical discussions, contributions and pointers to references for this manuscript. This research was partially supported by Army Research Lab (ARL) Grant W911NF-20-2-0158 under the cooperative A2I2 programme. A.K. was supported by an National Science Foundation (NSF) CAREER award IIS-2046737, Army Young Investigator Program Award, and Defense Advanced Research Projects Agency (DARPA) Young Faculty Award.

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  1. University of California, Los Angeles, Los Angeles, CA, USA

    Achuta Kadambi, Cho-Jui Hsieh, Mani Srivastava & Stefano Soatto

  2. DEVCOM United States Army Research Laboratory, Los Angeles, CA, USA

    Celso de Melo

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All authors contributed to the ideas in the manuscript. A.K. took the lead in coordinating the figures and writing the manuscript. S.S. and C.d.M. had a supporting role in writing the manuscript. All authors proofread the manuscript.

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Correspondence to Achuta Kadambi.

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A.K. is an employee, receives salary and owns stock in Intrinsic (an Alphabet company); and is a co-founder and owns stock in Vayu Robotics. C.d.M. declares no competing interests. C.-J.H., M.S. and S.S. hold employment, draw salary from and hold stock in Amazon.

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Kadambi, A., de Melo, C., Hsieh, CJ. et al. Incorporating physics into data-driven computer vision. Nat Mach Intell 5, 572–580 (2023). https://doi.org/10.1038/s42256-023-00662-0

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