Abstract
This paper attempts to address the issue of smart farming application, which targets discriminating distinct cocoa bean categories. In smart farming application, one critical issue is how to distinguish little difference among all categories. Our proposed scheme is designed to construct a more robust representation to better leverage textual information. The key concept is to adaptively accumulate contextual representations to obtain the contextual channel attention. Specifically, we introduce a contextual memory cell to progressively select the contextual channel-wise statistics. The accumulated contextual statistics are then used to explore the channel-wise relationship which implicitly correlates contextual channel states. Accordingly, we propose the progressive contextual excitation (PCE) module employing channel-attention-based architecture to simultaneously correlate the contextual channel-wise relationships. The progressive manner via the contextual memory cell demonstrates efficiently to guide high-level representation by keeping more detailed information, which benefits to discriminate small variations in tackling the smart farming application task. We evaluate our model on the cocoa beans dataset which comprises fine-grained cocoa bean categories. The experiments show a significant boost compared with existing approaches.
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The authors gratefully acknowledge the support by the Ministry of Science and Technology, Taiwan, under grant MOST-110-2221-E-011-013 -
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Bai, CH., Prakosa, S.W., Hsieh, HY., Leu, JS., Fang, WH. (2021). Progressive Contextual Excitation for Smart Farming Application. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_32
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DOI: https://doi.org/10.1007/978-3-030-89128-2_32
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