지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 868 - 876 (9page)
이용수
초록· 키워드
오류제보하기
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
참고문헌 (17)
참고문헌 신청
Ren, S., He, K., Girshick, R., & Sun, J. ‘‘Faster r-cnn: Towards real-time object detection with region proposal networks.’’ Advances in neural information processing systems, 28, pp.91-99, 2015
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. “ Focal loss for dense object detection. ’’ In Proceedings of the IEEE international conference on computer vision. p. 2980-2988, 2017.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. “Mask r-cnn.’’ In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
He, K., Zhang, X., Ren, S., & Sun, J. “Deep residual learning for image recognition.’’ In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. “Feature pyramid networks for object detection.’’ In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117-2125, 2017.
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. “ Focal loss for dense object detection. ’’ In Proceedings of the IEEE international conference on computer vision. p. 2980-2988, 2017.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. “Mask r-cnn.’’ In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
He, K., Zhang, X., Ren, S., & Sun, J. “Deep residual learning for image recognition.’’ In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. “Feature pyramid networks for object detection.’’ In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117-2125, 2017.
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