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Adaptive local recalibration network for scene recognition

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Abstract

Scene recognition is a computer vision task that categorizes scenes from photographs. In this paper, we introuduce the Adaptive Local Recalibration Network (ALR-Net), a novel scene recognition method based on convolutional neural networks (CNNs). In comparison to the object classification task, the scene classification images have a more dispersed distribution of information. To solve this issue, we suggest an attention mechanism for locating the discriminative regions for scene recognition. Along with normal data augmentation, we use the regions to guide two additional data augmentation approaches, namely adaptive cropping and adaptive hiding, in order to capture local information more efficiently and specifically. Attention maps are also used to adaptively recalibrate scene feature maps so that discriminative regions receive more attention than others. In addition, we bring in a scene distribution label for each image, which is used to assist the training of attention maps. Extensive studies on two scene recognition benchmarks verified the proposed model’s effectiveness: MIT67 (88.37%) and SUN397 (74.24%).

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Data Availability

All data generated or analysed during this study are included in these published articles [32, 48, 49].

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Funding

The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University. This work is supported by the National Natural Science Foundation of China Enterprise Innovation and Development Joint Fund (Project No. U19B2004).

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Jiale Wang, Lian Zou, Cien Fan, Hao Jiang, Liqiong Chen, Mofan Cheng & Hu Yu

  2. National Engineering Laboratory for Risk Perception and Prevention (NEL-RPP), Beijing, 100041, China

    Yifeng Liu

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  1. Jiale Wang
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Correspondence to Lian Zou.

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Wang, J., Zou, L., Fan, C. et al. Adaptive local recalibration network for scene recognition. Appl Intell 53, 27935–27950 (2023). https://doi.org/10.1007/s10489-023-04963-0

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