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Reflective field for pixel-Level tasks

Zhang, L., Kong, X., Shen, P., Zhu, G., Song, J., Shah, S.A.A. and Bennamoun, M. (2018) Reflective field for pixel-Level tasks. In: 24th International Conference on Pattern Recognition (ICPR) 2018, 20 - 24 August 2018, Beijing, China

Link to Published Version: https://doi.org/10.1109/ICPR.2018.8545817
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Abstract

PixelNet has achieved great success in dense prediction problems with a pure pixel-level architecture, but there is still much room for improvement. In this paper, we start from PixelNet and discuss the pixel-level architecture called hypercol-umn and its limitations in building feature representation with rich semantic information. To achieve this goal, we propose a concept in the context of neural networks called reflective field, representing the area reflected by the origin input. Furthermore, the proposed reflective field is used to solve the limitations of the hypercolumn architecture. Specifically, we give the method of calculating the size of the reflective field and analyze the effective reflective field in the calculated area. Then, we use the reflective field to build a new hypercolumn architecture, which has a more rational construction. The results on PASCAL VOC segmentation dataset with our new architecture are improved.

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/50051
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