• Korean Institute of Interior Design Journal
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• v.24 no.6
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• pp.145-153
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• 2015

For this research with the objects of spaces in two department stores which can be referred to as representative facility of commercial space, observation test has carried out to estimate how much visitors rivet their eyes to the display of shops. In addition, to find out what effect the difference among the department types has on the selective attention to space element, the observation time was applied as a medium for estimation. The followings are the result from analyzing the observation frequency and the observation intensity feature of each section where the characteristics of design could be found at attention. First, both images of A and B had concentrative dominant-observation at left shops. In case of Image A, Customers began to observe the right shops very attentively after 25 seconds, and with Image B, the attentive observation at right and left took place alternatively after 35 seconds. In other words, regardless of the characteristics of shop displays, the left shops were observed first while in case of the observation after the early and middle time-frame the characteristics of shops were found to have effects on observation. Second, the normal observation showed some difference among attention sections over time while on the whole both images of A and B had the same highly attentive observation at the middle space. Accordingly, it could be concluded that the middle space was playing a faithful role as background for commercial spaces. Third, the ignorant observation, which is the opposite to the attentive observation, was found different between the images of A and B. When the ignorant observation is considered to have intentionality, it will be possible to set up the display which may attract the attention aggressively by the process of figuring out the characteristics of ignored shops.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.50-58
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• 2007

In this paper, we propose a query-based visual attention algorithm for effective object finding of a vision-based mobile robot. This algorithm is developed by extending conventional bottom-up visual attention algorithms. In our proposed algorithm various conspicuity maps are merged to make a saliency map, where weighting values are determined by query-dependent object properties. The saliency map is then used to find possible attentive location of queried object. To show the validities of our proposed algorithm, several objects are employed to compare performances of our proposed algorithm with those of conventional bottom-up approaches. Here, as one of exemplar query-dependent object property, color property is used.

• ETRI Journal
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• v.44 no.6
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.