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논문 기본 정보

자료유형
학술저널
저자정보
Jung Heum Kang (Kyung Hee Univ.) Hye Won Jeong (Kyung Hee Univ.) Chang Kyun Choi (Kyung Hee Univ.) Muhammad Salman Ali (Kyung Hee Univ.) Sung-Ho Bae (Kyung Hee Univ.) Hui Yong Kim (Kyung Hee Univ.)
저널정보
한국방송·미디어공학회 방송공학회논문지 방송공학회논문지 제26권 제7호
발행연도
2021.12
수록면
868 - 876 (9page)

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초록· 키워드

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Deploying deep neural network model training performs remarkable performance in the fields of Object detection and Instance segmentation. To train these models, features are first extracted from the input image using a backbone network. The extracted features can be reused by various tasks. Research has been actively conducted to serve various tasks by using these learned features. In this process, standardization discussions about encoding, decoding, and transmission methods are proceeding actively. In this scenario, it is necessary to analyze the response characteristics of features against various distortions that may occur in the data transmission or data compression process. In this paper, experiment was conducted to inject various distortions into the feature in the object recognition task. And analyze the mAP (mean Average Precision) metric between the predicted value output from the neural network and the target value as the intensity of various distortions was increased. Experiments have shown that features are more robust to distortion than images. And this points out that using the feature as transmission means can prevent the loss of information against the various distortions during data transmission and compression process.

목차

Abstract
Ⅰ. Introduction
Ⅱ. Related works
Ⅲ. Method
Ⅳ. Experimental Result & Analysis
Ⅴ. Conclusion
References

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