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Master of All: Simultaneous Generalization of Urban-Scene Segmentation to All Adverse Weather Conditions

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Book cover Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13699))

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Abstract

Computer vision systems for autonomous navigation must generalize well in adverse weather and illumination conditions expected in the real world. However, semantic segmentation of images captured in such conditions remains a challenging task for current state-of-the-art (SOTA) methods trained on broad daylight images, due to the associated distribution shift. On the other hand, domain adaptation techniques developed for the purpose rely on the availability of the source data, (un)labeled target data and/or its auxiliary information (e.g., GPS). Even then, they typically adapt to a single(specific) target domain(s). To remedy this, we propose a novel, fully test time, adaptation technique, named Master of ALL (MALL), for simultaneous generalization to multiple target domains. MALL learns to generalize on unseen adverse weather images from multiple target domains directly at the inference time. More specifically, given a pre-trained model and its parameters, MALL enforces edge consistency prior at the inference stage and updates the model based on (a) a single test sample at a time (MALL-sample), or (b) continuously for the whole test domain (MALL-domain). Not only the target data, MALL also does not need access to the source data and thus, can be used with any pre-trained model. Using a simple model pre-trained on daylight images, MALL outperforms specially designed adverse weather semantic segmentation methods, both in domain generalization and test-time adaptation settings. Our experiments on foggy, snow, night, cloudy, overcast, and rainy conditions demonstrate the target domain-agnostic effectiveness of our approach. We further show that MALL can improve the performance of a model on an adverse weather condition, even when the model is already pre-trained for the specific condition.

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Notes

  1. 1.

    Note that we do not assume the availability of source data as our method does not need any training for the chosen backbone. However, we do assume the availability of the statistics for the source dataset on which a given model is pre-trained on.

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Acknowledgment

We thank UQ-IIT Delhi Research Academy (UQIDAR) for providing contingency grant. This work has also been partly supported by the funding received from DST through the IMPRINT program (IMP/2019/000250). We acknowledge National Super-computing Mission (NSM) for providing computing resources of ‘PARAM Siddhi-AI’, under National PARAM Super-computing Facility (NPSF), C-DAC, Pune, and supported by the Ministry of Electronics and Information Technology (MeitY) and Department of Science and Technology (DST), Government of India.

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Authors and Affiliations

  1. University of Queensland – IIT Delhi Academy of Research (UQIDAR), Delhi, India

    Nikhil Reddy

  2. Indian Institute of Technology Delhi, Delhi, India

    Abhinav Singhal, Abhishek Kumar & Chetan Arora

  3. The University of Queensland, Brisbane, Australia

    Mahsa Baktashmotlagh

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  1. Nikhil Reddy
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  2. Abhinav Singhal
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  3. Abhishek Kumar
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  4. Mahsa Baktashmotlagh
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  5. Chetan Arora
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Correspondence to Nikhil Reddy .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Reddy, N., Singhal, A., Kumar, A., Baktashmotlagh, M., Arora, C. (2022). Master of All: Simultaneous Generalization of Urban-Scene Segmentation to All Adverse Weather Conditions. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13699. Springer, Cham. https://doi.org/10.1007/978-3-031-19842-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-19842-7_4

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