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Graph Convolution Network Based Feature Map Fusion Method for Multi Scale Object Detection
Jaegi Hwang, Seongju Kang, Kwangsue Chung
http://doi.org/10.5626/JOK.2022.49.8.627
Feature Pyramid Network (FPN) is a feature map fusion technique used to solve the multi-scale problem of object detection. However, since FPN performs feature map fusion by focusing on adjacent resolutions, there is a problem in that semantic information included in non-adjacent layers is diluted. This paper, proposes a graph convolution network (GCN)-based feature map fusion technique for multi-scale object detection. The proposed GCN-based method dynamically fuses feature map information of all layers according to learnable adjacency matrix weights. The adjacency matrix weight is generated based on the multi-scale attention mechanism to adaptively reflect the scale information of the object. The feature map fusion process is performed through a matrix multiplication operation between adjacency matrix and a feature node matrix. The performance of the proposed method was verified by showing that it improves the multi-scale object detection performance in the PASCAL-VOC benchmark dataset compared to the existing FPN method.
Data Modelling Method for Real-Time Advertising Service Based on Viewer Reaction and Intention in Online Broadcasting
Seongju Kang, Chaeeun Jeong, Kwangsue Chung
http://doi.org/10.5626/JOK.2020.47.11.1086
The interaction between the existing advertising service and the user is limited. To provide a personalized advertising service, advertisement systems should predict the user"s preference based on the user"s profile and the user-content relationship. Many recommendation schemes have been studied to predict the preferences of users. However, the existing recommendation system is difficult to guarantee real-time preference prediction as it performs a calculation of the matrix with high computational complexity. In this paper, we propose a data modeling method for real-time advertising services based on the reaction and intention of viewers. To predict the user"s preference in real-time, the user"s historical data is modeled in a tree structure. The tree structure allows us to retrieve and compare the data with logarithmic time complexity. To improve the accuracy of the recommendation, we have proposed a recommendation algorithm that considers both the user"s positive and negative evaluations. Finally, we have evaluated the performance of the proposed method through various methods.
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