• Korean Journal of Child Studies
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• v.28 no.5
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• pp.297-307
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• 2007

This study investigated relationships between engagement in joint attention and the early language development in infancy. Subjects were 12 infants and their mothers. At 20 and 25 months of age, each child's spontaneous natural speech during interaction with his/her caregiver was videotaped for about 30 minutes. The EJA(Episodes of joint attention) focus between mother and child were identified and coded by Tomasello and Todd's(1983) and Bakeman and Adamson's(1984) including person engagement, object engagement, looking engagement, passive joint attention, coordinated joint attention. Results showed that a significant difference in infant's language development between within and outside EJA at 20 and 25 months of age; that is, during periods of EJA children talked more inside than outside EJA.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.300-306
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• 2012

An image attention model and its application to image quality assessment are discussed in this paper. The attention model is based on rarity quantification, which is related to self-information to attract the attention in an image. It is relatively simpler than the others but results in taking more consideration of global contrasts between a pixel and the whole image. The visual attention model is used to develop a local distortion predictor, named color visual differences predictor (CVDP), in color images in order to effectively detect luminance and color distortions.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.287-292
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• 2019

다양한 매체를 통해 텍스트 데이터가 빠르게 생성되면서 요약된 텍스트에 대한 수요가 증가하고 있다. 시퀀스-투-시퀀스 모델의 등장과 attention 기법의 출현은 추상적 요약의 난도를 낮추고 성능을 상승시켰다. 그러나 그동안 진행되어 온 attention 기반의 시퀀스-투-시퀀스 모델을 통한 요약 관련 연구들은 요약 시 텍스트의 카테고리 정보를 이용하지 않았다. 텍스트의 카테고리 정보는 Class Activation Map(CAM)을 통해 얻을 수 있는데, 텍스트를 요약할 때 핵심이 되는 단어와 CAM에서 높은 수치를 보이는 단어가 상당수 일치한다는 사실은 요약문 생성이 텍스트의 카테고리에 의존적일 필요가 있음을 증명한다. 본 논문에서는 요약문 생성 시 집중 정도에 대한 정보를 CAM을 통해 전달하여 attention matrix를 보강할 수 있는 모델을 제안하였다. 해당 모델을 사용하여 요약문을 생성하고 대표적인 요약 성능 지표인 ROUGE로 측정한 결과, attention 기반의 시퀀스-투-시퀀스 모델이 질이 떨어지는 요약문을 생성할 때 attention의 성능을 보강하여 요약문의 질을 높일 수 있음을 알 수 있었다.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.794-802
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• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.89-91
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• 2022

본 논문에서는 생체인식 수단 중 하나인 손금을 이용한 생체인식에서 Attention U-Net을 기반으로 손금을 추출하는 방법을 제안한다. 손바닥의 손금 중 주요선이라 불리는 생명선, 지능선, 감정선은 거의 변하지 않는 특징을 가지고 있다. 기존의 손금 추출 방법인 비슷한 색상에서 손금 추출, 제한된 Background에서 손금을 추출하는 것이 아닌 피부색과 비슷하거나, 다양한 Background에서 적용될 수 있다. 이를 통해 사용자를 인식하는 생체인식 방법에서 사용할 수 있다. 본 논문에서 사용된 Attention U-Net의 특징을 통해 손금의 Segmentation 영역을 Attention Coefficient를 업데이트하며 효율적으로 학습할 수 있음을 확인하였다.

• Journal of Korean Academy of Nursing
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• v.20 no.2
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• pp.214-226
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• 1990

For a man to maintain attention, he needs to keep a certain level of arousal. An inordinate increase or decrease in the level of arousal eventually has a negative influence on attention. Precedent research has shown that the degree of attention changes when an experience of stress is related to anxiety resulting in a rise in arousal. This research was done to examine this hypothesis by looking at the 27 female students, 14 of whom had failed in the annual examination. The results of the investigation are as follows : The stress of failure in the examination was seen to raise the level of physiological arousal. Although pulse and electromyography showed no significant change, further inquiries should be made based on other types of methodology. In spite of the rise of arousal, the performance of selective task was degraded. This suggests those students failed to give moderate attention to given information for that kind of task. But the exact reason of that failure was not identified : that is it was difficult decide whether they gave too much attention to the anxiety brought about by stress. Performance of integral tasks, however, did not show any degradation. Judging from these results, stress seems to exert significant influence on attention in the selection of the appropriate information among the various options given. This offers an important hint in relation to the health care situation where nursing information is offered. Clients who receive nursing information in stressful situations may have difficulty in separating and selecting this helpful information from other options which they have acquired through their life experience. The content and terminology of nursing information may be strange and unintelligible to clients, although they are quite familiar and distinct to nurses. So, it is desirable for nurses to give, in addition and at the same time when nursing information is given, some certain related information as devices for selection, instead of merely giving nursing informations as such. So far it is not clear whether the concepts of information processing theory can be suitably applied to nursing. However, it is obvious, according to this research, that the quality of attention is disturbed in the stress situation. This is why further inquiries should be made into attention in practical nursing situation.

• Korean Institute of Interior Design Journal
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• v.26 no.2
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• pp.3-12
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• 2017

This study has analyzed the features of attention to spacial composition seen in "Seeing ${\leftrightarrow}$ Seen" Correlation of continuous move in the space. The eye-tracking was employed for collecting the data of attention features to the space so that the correlation between visual perception and space could be estimated through the attention features to the difference between spacial composition and display. First, it was confirmed that the attention features varied according to the structure of shops and the exposure degree of selling space, which revealed that, while causing the customers' less attention to both sides of shops, the vanishing-point structure characteristically made their eyes focused on the central part. Second, their initial observation activities were found to be active at the height of their eyes. Third, 10 images were selected as objects for continuous experiment. There was a concern that the central part of each image would be paid intense attention to during the initial observation, but only two of those were found to be so. Fourth, there had been a study result of eye-tracking experiment that the attention had been concentrated on the central part of the image first seen. This study, however, revealed that such phenomenon is limited to the first image. Accordingly, it is necessary to draw up such method for ensuring reliability in order to use the data acquired from any eye-tracking experiment as exclusion of the initial attention time to the first image or of unemployment of the initial image-experiment to analysis.