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Benchmarking Omni-Vision Representation Through the Lens of Visual Realms

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

Though impressive performance has been achieved in specific visual realms (e.g. faces, dogs, and places), an omni-vision representation generalizing to many natural visual domains is highly desirable. But, existing benchmarks are biased and inefficient to evaluate the omni-vision representation—these benchmarks either only include several specific realms, or cover most realms at the expense of subsuming numerous datasets that have extensive realm overlapping. In this paper, we propose Omni-Realm Benchmark (OmniBenchmark). It includes 21 realm-wise datasets with 7,372 concepts and 1,074,346 images. Without semantic overlapping, these datasets cover most visual realms comprehensively and meanwhile efficiently. In addition, we propose a new supervised contrastive learning framework, namely Relational Contrastive learning (ReCo), for a better omni-vision representation. Beyond pulling two instances from the same concept closer—the typical supervised contrastive learning framework—ReCo also pulls two instances from the same semantic realm closer, encoding the semantic relation between concepts, facilitating omni-vision representation learning. We benchmark ReCo and other advances in omni-vision representation studies that are different in architectures (from CNNs to transformers) and in learning paradigms (from supervised learning to self-supervised learning) on OmniBenchmark. We illustrate the superior of ReCo to other supervised contrastive learning methods, and reveal multiple practical observations to facilitate future research. The code and models are available at https://zhangyuanhan-ai.github.io/OmniBenchmark.

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Notes

  1. 1.

    Annotation budget also limits its realm coverage.

  2. 2.

    We use “Omni” to emphasize the diversity of semantic realms.

  3. 3.

    https://www.flickr.com/, All the crawled images are strictly followed the CC-BY license.

  4. 4.

    The other 185 concepts are included in realm that are filtered in Sect. 3.3.

  5. 5.

    In the following material, the dataset-seen-realms/dataset-unseen-realms is a set of realms. Each dataset-seen-realm/dataset-unseen-realm in it has at least 20 concepts/fewer than 20 concepts.

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Acknowledgement

This work is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund—Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

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Authors and Affiliations

  1. S-Lab, Nanyang Technological University, Singapore, Singapore

    Yuanhan Zhang & Ziwei Liu

  2. SenseTime Research, Shatin, Hong Kong

    Zhenfei Yin & Jing Shao

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  1. Yuanhan Zhang
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  2. Zhenfei Yin
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  3. Jing Shao
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  4. Ziwei Liu
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Correspondence to Jing Shao .

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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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Zhang, Y., Yin, Z., Shao, J., Liu, Z. (2022). Benchmarking Omni-Vision Representation Through the Lens of Visual Realms. 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 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_35

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