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X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

In computer vision, pre-training models based on large-scale supervised learning have been proven effective over the past few years. However, existing works mostly focus on learning from individual task with single data source (e.g., ImageNet for classification or COCO for detection). This restricted form limits their generalizability and usability due to the lack of vast semantic information from various tasks and data sources. Here, we demonstrate that jointly learning from heterogeneous tasks and multiple data sources contributes to universal visual representation, leading to better transferring results of various downstream tasks. Thus, learning how to bridge the gaps among different tasks and data sources is the key, but it still remains an open question. In this work, we propose a representation learning framework called X-Learner, which learns the universal feature of multiple vision tasks supervised by various sources, with expansion and squeeze stage: 1) Expansion Stage: X-Learner learns the task-specific feature to alleviate task interference and enrich the representation by reconciliation layer. 2) Squeeze Stage: X-Learner condenses the model to a reasonable size and learns the universal and generalizable representation for various tasks transferring. Extensive experiments demonstrate that X-Learner achieves strong performance on different tasks without extra annotations, modalities and computational costs compared to existing representation learning methods. Notably, a single X-Learner model shows remarkable gains of 3.0%, 3.3% and 1.8% over current pre-trained models on 12 downstream datasets for classification, object detection and semantic segmentation.

Y. He, G. Huang, S. Chen, J. Teng—Equal contribution.

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Notes

  1. 1.

    To avoid ambiguity, we refer to a task as a general vision problem such as classification, detection or segmentation, and a source as a specific dataset or context within a certain task.

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Acknowledgements

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) and the Shanghai Committee of Science and Technology (Grant No. 21DZ1100100).

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Authors and Affiliations

  1. Shanghai AI Laboratory, Shanghai, China

    Yinan He & Yu Qiao

  2. Sun Yat-sen University, Guangzhou, China

    Gengshi Huang

  3. Carnegie Mellon University, Pittsburgh, USA

    Siyu Chen

  4. SenseTime Research, Sha Tin, Hong Kong

    Jianing Teng, Kun Wang, Zhenfei Yin & Jing Shao

  5. College of Software, Beihang University, Beijing, China

    Lu Sheng

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

    Ziwei Liu

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  1. Yinan He
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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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He, Y. et al. (2022). X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation. 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 13686. Springer, Cham. https://doi.org/10.1007/978-3-031-19809-0_29

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