Global Context Guided Multi-scale Feature Network for Salient Object Detection

2021 ◽  
pp. 81-89
Author(s):  
Zhenyu Zhao ◽  
Yachao Fang ◽  
Qing Zhang ◽  
Xiaowei Chen ◽  
Meng Dai ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Global Context ◽  
Scale Feature ◽  
Multi Scale

LGCNet: A Local-to-global Context-aware Feature Augmentation Network for Salient Object Detection

2021 ◽  
Author(s):  
Yuzhu Ji ◽  
Haijun Zhang ◽  
Feng Gao ◽  
Haofei Sun ◽  
Haokun Wei ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Context Aware ◽  
Salient Object ◽  
Global Context ◽  
Feature Augmentation

Multi-level and multi-scale deep saliency network for salient object detection

2019 ◽  
Vol 59 ◽  
pp. 415-424 ◽  
Author(s):  
Qing Zhang ◽  
Jiajun Lin ◽  
JingJing Zhuge ◽  
Wenhao Yuan
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Multi Scale ◽  
Multi Level

scholarly journals Global Context-Aware Progressive Aggregation Network for Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 10599-10606 ◽  
Author(s):  
Zuyao Chen ◽  
Qianqian Xu ◽  
Runmin Cong ◽  
Qingming Huang
Keyword(s):  
Object Detection ◽  
Critical Role ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Semantic Features ◽  
Context Aware ◽  
Salient Object ◽  
Deep Convolutional Neural Networks ◽  
Global Context ◽  
High Level

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple-level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Salient object detection via multi-scale attention CNN

2018 ◽  
Vol 322 ◽  
pp. 130-140 ◽  
Author(s):  
Yuzhu Ji ◽  
Haijun Zhang ◽  
Q.M. Jonathan Wu
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Multi Scale
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