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Model Architecture Analysis and Extension for Improving RF-based Multi-Person Pose Estimation Performance
http://doi.org/10.5626/JOK.2024.51.3.262
An RF-based multi-person pose estimation system can estimate each human posture even when it is challenging to obtain clear visibility due to obstacles or lighting conditions. Traditionally, a cross-modal teacher-student learning approach has been employed. The approach utilizes pseudo-label data acquired by using images captured concurrently with RF signal collection as input for a pretrained image-based pose estimation model. In a previous research study, the research team applied cross-modal knowledge distillation to mimic the feature maps of image-based learning models and referred to it as "visual cues." This enhanced the performance of RF signal-based pose estimation. In this paper, performance is compared based on the ratio at which the learned visual cues are concatenated, and an analysis of the impact of segmentation mask learning and the use of multiframe inputs on multi-person pose estimation performance is presented. It is demonstrated that the best performance is achieved when visual cues and multiframe inputs are used in combination.
Analyzing the Impact of Sequential Context Learning on the Transformer Based Korean Text Summarization Model
Subin Kim, Yongjun Kim, Junseong Bang
http://doi.org/10.5626/JOK.2021.48.10.1097
Text summarization reduces the sequence length while maintaining the meaning of the entire article body, solving the problem of overloading information and helping readers consume information quickly. To this end, research on a Transformer-based English text summarization model has been actively conducted. Recently, an abstract text summary model reflecting the characteristics of English with a fixed word order by adding a Recurrent Neural Network (RNN)-based encoder was proposed. In this paper, we study the effect of sequential context learning on the text abstract summary model by using an RNN-based encoder for Korean, which has more free word order than English. Transformer-based model and a model that added RNN-based encoder to existing Transformer model are trained to compare the performance of headline generation and article body summary for the Korean articles collected directly. Experiments show that the model performs better when the RNN-based encoder is added, and that sequential contextual information learning is required for Korean abstractive text summarization.
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