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Small Object Recognition Based on the Generative Adversarial Network and Multi-instance Learning

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12836))

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Abstract

In recent years, object recognition has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze that the limitation of existing algorithms for small target detection, such as: (1) the high computational overhead of image resolution increase and (2) the non-semantic data augmentation of small-object- copy-based strategy, leading a worse result in mAP. So, we figure out that the limited number of semantic training samples is a key impediment for this task due to the high cost of collecting and labelling nature images. In this paper, we propose a simple but effective framework for small object recognition. With an improved generative model, we propose a multiply instance learning detector based on CNN, which jointly learns from the labeled nature datasets and unlabeled generated images. Our method shows a state-of-the-art performance for small objects, obtained by Mask R-CNN, on MS COCO.

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Acknowledgments

This work is supported by Xiamen Major Science and Technology Projects (No. 3502Z20201017).

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Authors and Affiliations

  1. Xiamen Road and Bridge Information Co. Ltd., Floor 18, Building A06, Software Park Phase III, Xiamen, 361000, Fujian, China

    Lin Zhiyong

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  1. Lin Zhiyong
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Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

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Zhiyong, L. (2021). Small Object Recognition Based on the Generative Adversarial Network and Multi-instance Learning. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_39

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  • DOI: https://doi.org/10.1007/978-3-030-84522-3_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84521-6

  • Online ISBN: 978-3-030-84522-3

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