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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 288–307Cite as

k-means Mask Transformer

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13689))

Abstract

The rise of transformers in vision tasks not only advances network backbone designs, but also starts a brand-new page to achieve end-to-end image recognition (e.g., object detection and panoptic segmentation). Originated from Natural Language Processing (NLP), transformer architectures, consisting of self-attention and cross-attention, effectively learn long-range interactions between elements in a sequence. However, we observe that most existing transformer-based vision models simply borrow the idea from NLP, neglecting the crucial difference between languages and images, particularly the extremely large sequence length of spatially flattened pixel features. This subsequently impedes the learning in cross-attention between pixel features and object queries. In this paper, we rethink the relationship between pixels and object queries, and propose to reformulate the cross-attention learning as a clustering process. Inspired by the traditional k-means clustering algorithm, we develop a \(\textbf{k}\)-means Mask Xformer (kMaX-DeepLab) for segmentation tasks, which not only improves the state-of-the-art, but also enjoys a simple and elegant design. As a result, our kMaX-DeepLab achieves a new state-of-the-art performance on COCO val set with 58.0% PQ, and Cityscapes val set with 68.4% PQ, 44.0% AP, and 83.5% mIoU without test-time augmentation or external dataset. We hope our work can shed some light on designing transformers tailored for vision tasks. Code and models are available at https://github.com/google-research/deeplab2.

Work done during an internship at Google.

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Acknowledgments

We thank Jun Xie for the valuable feedback on the draft. This work was supported in part by ONR N00014-21-1-2812.

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Authors and Affiliations

  1. Johns Hopkins University, Baltimore, USA

    Qihang Yu, Huiyu Wang & Alan Yuille

  2. Google Research, Mountain View, USA

    Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam & Liang-Chieh Chen

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  1. Qihang Yu
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Correspondence to Qihang Yu .

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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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Yu, Q. et al. (2022). k-means Mask 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 13689. Springer, Cham. https://doi.org/10.1007/978-3-031-19818-2_17

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