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Class-Agnostic Object Detection with Multi-modal Transformer

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. For the first time in literature, we demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on the observation that existing MViTs do not include multi-scale feature processing and usually require longer training schedules, we develop an efficient MViT architecture using multi-scale deformable attention and late vision-language fusion. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs can adaptively generate proposals given a specific language query and thus offer enhanced interactability. Code: https://git.io/J1HPY.

M. Maaz and H. Rasheed—Equal contribution.

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Acknowledgements

Ming-Hsuan Yang is supported by the NSF CAREER grant 1149783. Fahad Shahbaz Khan is supported by the VR starting grant (2016-05543).

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Authors and Affiliations

  1. Mohamed bin Zayed University of AI, Abu Dhabi, UAE

    Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan & Rao Muhammad Anwer

  2. Australian National University, Canberra, Australia

    Salman Khan

  3. Linköping University, Linkoping, Sweden

    Fahad Shahbaz Khan

  4. Aalto University, Espoo, Finland

    Rao Muhammad Anwer

  5. University of California, Merced, Merced, USA

    Ming-Hsuan Yang

  6. Yonsei University, Seoul, South Korea

    Ming-Hsuan Yang

  7. Google Research, Mountain View, USA

    Ming-Hsuan Yang

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  1. Muhammad Maaz
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Correspondence to Muhammad Maaz .

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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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Maaz, M., Rasheed, H., Khan, S., Khan, F.S., Anwer, R.M., Yang, MH. (2022). Class-Agnostic Object Detection with Multi-modal Transformer. 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 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_30

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