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Highly Efficient Salient Object Detection with 100K Parameters

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

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Abstract

Salient object detection models often demand a considerable amount of computation cost to make precise prediction for each pixel, making them hardly applicable on low-power devices. In this paper, we aim to relieve the contradiction between computation cost and model performance by improving the network efficiency to a higher degree. We propose a flexible convolutional module, namely generalized OctConv (gOctConv), to efficiently utilize both in-stage and cross-stages multi-scale features, while reducing the representation redundancy by a novel dynamic weight decay scheme. The effective dynamic weight decay scheme stably boosts the sparsity of parameters during training, supports learnable number of channels for each scale in gOctConv, allowing 80% of parameters reduce with negligible performance drop. Utilizing gOctConv, we build an extremely light-weighted model, namely CSNet, which achieves comparable performance with \({\sim }0.2\%\) parameters (100k) of large models on popular salient object detection benchmarks. The source code is publicly available at https://mmcheng.net/sod100k/.

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Acknowledgements

Ming-Ming Cheng is the corresponding author. This research was supported by Major Project for New Generation of AI under Grant No. 2018AAA0100400, NSFC (61922046), Tianjin Natural Science Foundation (18ZXZNGX00110), and the Fundamental Research Funds for the Central Universities, Nankai University (63201169).

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Authors and Affiliations

  1. College of Computer Science, Nankai University, Tianjin, China

    Shang-Hua Gao, Yong-Qiang Tan, Ming-Ming Cheng & Chengze Lu

  2. Yitu Technology, Shanghai, China

    Yunpeng Chen & Shuicheng Yan

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  1. Shang-Hua Gao
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  2. Yong-Qiang Tan
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  4. Chengze Lu
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Correspondence to Ming-Ming Cheng .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Gao, SH., Tan, YQ., Cheng, MM., Lu, C., Chen, Y., Yan, S. (2020). Highly Efficient Salient Object Detection with 100K Parameters. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_42

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