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ECO-TR: Efficient Correspondences Finding via Coarse-to-Fine Refinement

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13670))

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Abstract

Modeling sparse and dense image matching within a unified functional correspondence model has recently attracted increasing research interest. However, existing efforts mainly focus on improving matching accuracy while ignoring its efficiency, which is crucial for real-world applications. In this paper, we propose an efficient structure named Efficient Correspondence Transformer (\({\textbf {ECO-TR}}\)) by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional correspondence model. To achieve this, multiple transformer blocks are stage-wisely connected to gradually refine the predicted coordinates upon a shared multi-scale feature extraction network. Given a pair of images and for arbitrary query coordinates, all the correspondences are predicted within a single feed-forward pass. We further propose an adaptive query-clustering strategy and an uncertainty-based outlier detection module to cooperate with the proposed framework for faster and better predictions. Experiments on various sparse and dense matching tasks demonstrate the superiority of our method in both efficiency and effectiveness against existing state-of-the-arts. Project page: https://dltan7.github.io/ecotr/.

D. Tan and J.-J. Liu—Authors contributed equally.

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Acknowledgments

This work was supported by the National Science Fund for Distinguished Young Scholars (No. 62025603), the National Natural Science Foundation of China (No. U21B2037, No. 62176222, No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, and No. 62002305), Guangdong Basic and Applied Basic Research Foundation (No. 2019B1515120049), and the Natural Science Foundation of Fujian Province of China (No. 2021J01002).

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Authors and Affiliations

  1. Media Analytics and Computing Lab, School of Informatics, Xiamen University, Xiamen, China

    Dongli Tan & Rongrong Ji

  2. TMCC, CS, Nankai University, Tianjin, China

    Jiang-Jiang Liu

  3. Youtu Lab, Tencent Technology (Shanghai) Co., Ltd., Shanghai, China

    Dongli Tan, Jiang-Jiang Liu, Xingyu Chen, Chao Chen, Ruixin Zhang, Yunhang Shen & Shouhong Ding

  4. Institute of Artificial Intelligence, Xiamen University, Xiamen, China

    Rongrong Ji

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  1. Dongli Tan
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  2. Jiang-Jiang Liu
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  3. Xingyu Chen
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  4. Chao Chen
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Correspondence to Rongrong Ji .

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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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Tan, D. et al. (2022). ECO-TR: Efficient Correspondences Finding via Coarse-to-Fine Refinement. 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_19

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