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Camouflaged Object Detection via Scale-Feature Attention and Type-Feature Attention

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15042))

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Abstract

Camouflaged object detection targets at identifying and segmenting objects hidden in the surroundings. Due to the various shapes and sizes, and highly non-discriminative features of camouflaged objects, it is a challenge for Convolutional Neural Networks (CNNs) to detect them from the background. To tackle the first problem of various shapes and sizes, we propose a Scale-Feature Attention (SFA), which can effectively integrate feature information of different scales, so that the model can comprehensively perceive and understand the visual characteristics of different sizes of camouflaged objects. Additionally, the traditional CNN model is difficult to capture the part-whole relationship of camouflaged objects. To solve the second problem of CNNs’ limitation, we propose a Type-Feature Attention (TFA) to integrate contrast from CNNs and part-whole relations from CapsNets, which will improve the identification and object wholeness of camouflaged objects. Experiments on three camouflaged object detection benchmark datasets show that both the proposed SFA and TFA achieve significant performance improvement, which verifies the superiority of the proposed method.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of Jiangsu Province under Grant BK20221379; the National Natural Science Foundation of China under Grant 62306048; the Changzhou University CNPC-CZU Innovation Alliance under Grant CCIA2023-01; the Chang-zhou Leading Innovative Talent Introduction and Cultivation Project under Grant 20221460; and by the Changzhou Applied Basic Research Fund Project Grant CQ20230092 and CJ20235036.

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, and School of Software, Changzhou University, Changzhou, China

    Yi Liu & Hui Meng

Authors
  1. Yi Liu
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  2. Hui Meng
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Corresponding author

Correspondence to Yi Liu .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Liu, Y., Meng, H. (2025). Camouflaged Object Detection via Scale-Feature Attention and Type-Feature Attention. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15042. Springer, Singapore. https://doi.org/10.1007/978-981-97-8858-3_14

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  • DOI: https://doi.org/10.1007/978-981-97-8858-3_14

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  • Online ISBN: 978-981-97-8858-3

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