Attention Block Based on Binary Pooling
نویسندگان
چکیده
Image classification has become highly significant in the field of computer vision due to its wide array applications. In recent years, Convolutional Neural Networks (CNN) have emerged as potent tools for addressing this task. Attention mechanisms offer an effective approach enhance accuracy image classification. Despite Global Average Pooling (GAP) being a crucial component traditional attention mechanisms, it only computes average spatial elements each channel, failing capture complete range feature information, resulting fewer and less expressive features. To address limitation, we propose novel pooling operation named “Binary Pooling” integrate into block. Binary combines both GAP Max (GMP), obtaining more comprehensive vector by extracting maximum values, thereby enriching diversity extracted Furthermore, further extraction features, dilation operations pointwise convolutions are applied on channel-wise. The proposed block is simple yet effective. Upon integration ResNet18/50 models, leads improvements 2.02%/0.63% ImageNet.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app131810012