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CFA-Net: Cross-Level Feature Fusion and Aggregation Network for Salient Object Detection

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

Salient object detection (SOD) is a research hotspot yet challenging task in computer vision, aiming at locating the most visually distinctive objects in an image. Recently, several deep learning based SOD models have been developed and achieved promising performance. Due to the salient variations among different convolutional layers, it is still a challenge to fuse the multi-level features and obtain better prediction performance. In this paper, we propose a Cross-level Feature fusion and Aggregation Network (CFA-Net) for SOD, which effectively integrates cross-level features to boost salient object performance. Specifically, an Adjacent Fusion Module (AFM) is proposed to effectively integrate the location information from low-level features with the rich semantic information of high-level features. Then, a Cascaded Feature Aggregation Module (CFAM) is proposed to further process the fused feature map, which is conducive to avoiding the introduction of redundant information and makes the features of the fusion feature map more distinctive. Unlike existing decoders, CFAM is able to enhance its own scale information while collecting the multi-scale information provided by the encoder, while allowing adjacent scale features to gain. The Hybrid-loss is introduced to assign weights for different pixels, making the network that focuses on details and boundaries. Extensive experimental results on several public datasets show the effectiveness of the proposed CFA-Net over other state-of-the-art SOD methods.

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Author information

Authors and Affiliations

  1. Qingdao University of Science and Technology, Qingdao, 266100, China

    Jingyi Liu

  2. Beijing Forestry University, Beijing, 100091, China

    Huiqi Li

  3. Ocean University of China, Qingdao, 266100, China

    Lin Li

  4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Tao Zhou

  5. Qingdao University, Qingdao, 266075, China

    Chenglizhao Chen

Authors
  1. Jingyi Liu
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  2. Huiqi Li
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  3. Lin Li
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  4. Tao Zhou
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  5. Chenglizhao Chen
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Corresponding author

Correspondence to Tao Zhou .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

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

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

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

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Liu, J., Li, H., Li, L., Zhou, T., Chen, C. (2022). CFA-Net: Cross-Level Feature Fusion and Aggregation Network for Salient Object Detection. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_36

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  • DOI: https://doi.org/10.1007/978-3-031-18916-6_36

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-18916-6

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