Abstract
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 hypercolumn
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.
| Original language | English |
|---|---|
| Title of host publication | Reflective Field for Pixel-Level Tasks |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 529-534 |
| ISBN (Print) | 9781538637883 |
| DOIs | |
| Publication status | Published - 10 Oct 2018 |
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Reflective Field for Pixel-Level Tasks. / Zhang, Liang; Kong, X.; Shen, Peiyi; Zhu, Guangming; Shah, Syed; Bennamoun, Mohammed; Song, J.
Reflective Field for Pixel-Level Tasks. IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 529-534.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Reflective Field for Pixel-Level Tasks
AU - Zhang, Liang
AU - Kong, X.
AU - Shen, Peiyi
AU - Zhu, Guangming
AU - Shah, Syed
AU - Bennamoun, Mohammed
AU - Song, J.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - PixelNet has achieved great success in dense predictionproblems with a pure pixel-level architecture, but there isstill much room for improvement. In this paper, we start fromPixelNet and discuss the pixel-level architecture called hypercolumnand its limitations in building feature representation withrich semantic information. To achieve this goal, we propose aconcept 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 ofthe hypercolumn architecture. Specifically, we give the method ofcalculating the size of the reflective field and analyze the effectivereflective field in the calculated area. Then, we use the reflectivefield to build a new hypercolumn architecture, which has a morerational construction. The results on PASCAL VOC segmentationdataset with our new architecture are improved.
AB - PixelNet has achieved great success in dense predictionproblems with a pure pixel-level architecture, but there isstill much room for improvement. In this paper, we start fromPixelNet and discuss the pixel-level architecture called hypercolumnand its limitations in building feature representation withrich semantic information. To achieve this goal, we propose aconcept 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 ofthe hypercolumn architecture. Specifically, we give the method ofcalculating the size of the reflective field and analyze the effectivereflective field in the calculated area. Then, we use the reflectivefield to build a new hypercolumn architecture, which has a morerational construction. The results on PASCAL VOC segmentationdataset with our new architecture are improved.
U2 - 10.1109/ICPR.2018.8545817
DO - 10.1109/ICPR.2018.8545817
M3 - Conference paper
SN - 9781538637883
SP - 529
EP - 534
BT - Reflective Field for Pixel-Level Tasks
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -