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Person Re-Identification Using an Attention Pyramid for Local Multiscale Feature Embedding Extracted from a Person’s Image
http://doi.org/10.5626/JOK.2021.48.12.1305
In this paper, a person re-identification scheme using the dual pyramid adapting attention mechanisms to extract more elaborate local feature embedding by excluding the noises caused by the unnecessary backgrounds in person’s image is proposed. With the dual pyramid of local and scale ones, the spatial attention is used to suppress the noise effects caused by unnecessary backgrounds, and the channel attention is used to emphasize the relatively important multiscale features when the local feature embedding is constructed. In the experiments, the proposed scheme was compared with other cases in which the attention module is not used for each pyramid to confirm the optimal configuration and compared based on the rank-1 accuracy with the state-of-the-art studies for the person re-identification. According to the experimental results, the proposed method showed a maximum rank-1 accuracy of 99.4%, which is higher by at least about 0.2% and at most by about 13.8% than previous works.
Efficient CNNs with Channel Attention and Group Convolution for Facial Expression Recognition
MyeongOh Lee, Ui Nyoung Yoon, Seunghyun Ko, Geun-Sik Jo
http://doi.org/10.5626/JOK.2019.46.12.1241
Recently, studies using the convolutional neural network have been actively conducted to recognize emotions from facial expressions. In this paper, we propose an efficient convolutional neural network that solves the model complexity problem of the deep convolutional neural network used to recognize the emotions in facial expression. To reduce the complexity of the model, we used group convolution, depth-wise separable convolution to reduce the number of parameters, and the computational cost. We also enhanced the reuse of features and channel information by using Skip Connection for feature connection and Channel Attention. Our method achieved 70.32% and 85.23% accuracy on FER2013, RAF-single datasets with four times fewer parameters (0.39 Million, 0.41 Million) than the existing model.
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