CA-CMT: Coordinate Attention for Optimizing CMT Networks
نویسندگان
چکیده
Vision Transformer (ViT) has been proposed as a new image recognition method in the field of computer vision. ViT applies structure with excellent performance natural language processing to recognize images. Unlike existing Convolutional Neural Network (CNN) models, can achieve State-Of-The-Art (SOTA) without inputting Inductive Biases into model, demonstrating that is useful However, requires large datasets such ImageNet-21K and Joint Foto Tree (JFT) for learning. In addition, it takes lot time train. Moreover, there problem location information lost by images patch units. To improve issues, many models are being proposed. this paper, model restructuring neural networks Meet vision Transformers (CMT) applying Coordinate Attention Block, CNN problems family models. The combines Transformer, which shown Long Range, CNN, Local Feature, higher than We also compared those relatively small Canadian Institute For Advanced Research-10 (CIFAR-10), Self-Taught Learning-10 (STL-10), Tiny-ImageNet facilitate researchers’ access evaluation. Despite restructured from smallest CMT-Tiny showed better accuracy CMT-Tiny, CMT-XS, CMT-S, CMT-B CIFAR-10, STL-10, datasets. an 90.21% CIFAR-10 dataset, CMT except CMT-S 90.6%. It had lowest loss value 0.3967. expected be utilized backbone Object Detection Segmentation fields future.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3297206