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Asian Conference on Computer Vision

ACCV 2020: Computer Vision – ACCV 2020 pp 369–387Cite as

Few-Shot Object Detection by Second-Order Pooling

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12625))

Abstract

In this paper, we tackle a challenging problem of Few-shot Object Detection rather than recognition. We propose Power Normalizing Second-order Detector consisting of the Encoding Network (EN), the Multi-scale Feature Fusion (MFF), Second-order Pooling (SOP) with Power Normalization (PN), the Hyper Attention Region Proposal Network (HARPN) and Similarity Network (SN). EN takes support image crops and a query image per episode to produce covolutional feature maps across several layers while MFF combines them into multi-scale feature maps. SOP aggregates them per support image while PN detects the presence of visual feature instead of counting its frequency of occurrence. HARPN cross-correlates the PN pooled support features against the query feature map to match regions and produce query region proposals that are then aggregated with SOP/PN. Finally, support and query second-order descriptors are passed to SN.

Our approach performs well because: (i) HARPN leverages SOP/PN for cross-correlation of detected rather than counted support features with query features which improves region proposals, (ii) SOP/PN capture second-order statistics per region proposal and factor out spatial locations, and (iii) PN limits the complexity of the space of functions over which HARPN and SN learn. These properties lead to the state of the art on the PASCAL VOC 2007/12, MS COCO and the FSOD datasets.

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Notes

  1. 1.

    FSOD is a new dataset released in 2020. Its format differs from datasets such as VOC and COCO. Only few approaches (listed in Table 3) experimented on FSOD.

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Author information

Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Shan Zhang & Piotr Koniusz

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Dawei Luo

  3. University of Wollongong, Wollongong, Australia

    Lei Wang

  4. Data61/CSIRO, Clayton South, Australia

    Piotr Koniusz

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  1. Shan Zhang
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  2. Dawei Luo
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  3. Lei Wang
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  4. Piotr Koniusz
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Corresponding author

Correspondence to Piotr Koniusz .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Zhang, S., Luo, D., Wang, L., Koniusz, P. (2021). Few-Shot Object Detection by Second-Order Pooling. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_23

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