Multidomain feature fusion method for small object classification: MDFF
نویسندگان
چکیده
The task of classifying small objects is still challenging for current deep learning classification models [such as convolutional neural networks (CNNs) and vision transformers (ViTs)]. We believe that these algorithms are not designed specifically targets, so their feature extraction abilities targets insufficient. To improve the capabilities CNN-based ViT-based objects, two multidomain fusion (MDFF) frameworks proposed to increase amount information derived from images they called MDFF-ConvMixer MDFF-ViT. Compared with basic model, uniquely added design includes frequency domain MDFF processes. In part, input image first transformed into a form through discrete cosine transform (DCT) transformation then three-dimensional matrix containing obtained via channel splicing reshaping. splices spatial features by channel, whereas MDFF-ViT uses cross-attention mechanism fuse features. When targeting target tasks, obviously utilized algorithm. On DOTA dataset CIFAR10 downsampling operations, accuracies relative ConvMixer 87.82% 62.14% 90.14% 66.00%, respectively, ViT 79.22% 36.2% 88.15% 59.23%, respectively.
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ژورنال
عنوان ژورنال: Journal of Electronic Imaging
سال: 2023
ISSN: ['1017-9909', '1560-229X']
DOI: https://doi.org/10.1117/1.jei.32.4.043009