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Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13680))

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Abstract

Few-shot object detection is based on the base set with abundant labeled samples to detect novel categories with scarce samples. The majority of former solutions are mainly based on meta-learning or transfer-learning, neglecting the fact that images from the base set might contain unlabeled novel-class objects, which easily leads to performance degradation and poor plasticity since those novel objects are served as the background. Based on the above phenomena, we propose a Mutually Reinforcing Structure Network (MRSN) to make rational use of unlabeled novel class instances in the base set. In particular, MRSN consists of a mining model which unearths unlabeled novel-class instances and an absorbed model which learns variable knowledge. Then, we design a Proposal Contrastive Consistency (PCC) module in the absorbed model to fully exploit class characteristics and avoid bias from unearthed labels. Furthermore,we propose a simple and effective data synthesis method undirectional-CutMix (UD-CutMix) to improve the robustness of model mining novel class instances, urge the model to pay attention to discriminative parts of objects and eliminate the interference of background information. Extensive experiments illustrate that our proposed approach achieves state-of-the-art results on PASCAL VOC and MS-COCO datasets. Our code will be released at https://github.com/MMatx/MRSN.

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Acknowledgment

This work is supported by National Key Research and Development Program of China (2019YFC1521104, 2021ZD0111000), National Natural Science Foundation of China (72192821, 61972157, 62176092, 62106075), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), Art major project of National Social Science Fund (I8ZD22), Shanghai Science and Technology Commission (21511101200, 22YF1420300, 21511100700), Natural Science Foundation of Shanghai (20ZR1417700), CAAI-Huawei MindSpore Open Fund.

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Authors and Affiliations

  1. East China Normal University, Shanghai, China

    Tianxue Ma, Wang Yuan, Zhizhong Zhang, Yuan Xie & Lizhuang Ma

  2. Tencent Youtu Lab, Shanghai, China

    Mingwei Bi, Jian Zhang & Shouhong Ding

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  1. Tianxue Ma
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  2. Mingwei Bi
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  3. Jian Zhang
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  4. Wang Yuan
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  5. Zhizhong Zhang
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  6. Yuan Xie
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  7. Shouhong Ding
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  8. Lizhuang Ma
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Correspondence to Zhizhong Zhang or Lizhuang Ma .

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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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Ma, T. et al. (2022). Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection. 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 13680. Springer, Cham. https://doi.org/10.1007/978-3-031-20044-1_23

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  • DOI: https://doi.org/10.1007/978-3-031-20044-1_23

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