Paper Info
Title
Bi-Connect Net for salient object detection.
Abstract
As a challenging task for pixel-wise image analysis, salient object detection has made huge progress in recent years. However, there still exists a difficult problem: detection of distinguishing a salient and non-salient object in multiple objects under complex background (e.g. blur, translucent, light reflection, etc.). Our proposed method cast this difficulty as information dissolve problem in deep convolutional network, which is manifested as: first, the model cannot grab whole details of a salient object at training phrase; second, due to the isolation between layers and blocks, the valued information is blocked within a block, which leads to the difficulty in obtaining the position and the edge of salient objects simultaneously; third, the output of the network is a low-resolution saliency map, which cannot accurately express the edge of salient objects. To address information dissolve problems, we construct a Bi-Connect Net (BCN) composed of forward connection subnet and reverse side connection subnet. Besides, the proposed adaptive learning fusion method not only stress all blocks contribution but also combine multiple features with different scale, so that grab more details on the right salient location and precise edges at the same time. Extensive experiments show that our proposed Bi-Connect Net can outperform the state-of-the-art methods.
Year
DOI
Venue
2020
10.1016/j.neucom.2019.12.020
Neurocomputing
Keywords
Field
DocType
Salient object detection,Forward connection,Reverse side connection
Salient object detection,Pattern recognition,Salient objects,Phrase,Subnet,Light reflection,Difficult problem,Artificial intelligence,Adaptive learning,Mathematics,Salient
Journal
Volume
ISSN
Citations 
384
0925-2312
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Fengwei Jia113.07
Xuan Wang229157.12
GUAN Jian34715.77
Qing Liao43711.60
Zhang Jiajia536.01
Huale Li611.38
Shuhan Qi73814.95