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.

scholarly journals Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection

2021 ◽  
Vol 13 (23) ◽  
pp. 4941
Author(s):  
Rukhshanda Hussain ◽  
Yash Karbhari ◽  
Muhammad Fazal Ijaz ◽  
Marcin Woźniak ◽  
Pawan Kumar Singh ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Similarity Index ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Semantic Features ◽  
Salient Object ◽  
Fully Convolutional Neural Networks ◽  
Boundary Estimation ◽  
Prediction Module

Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures.

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

Salient object detection with high‐level prior based on Bayesian fusion

2017 ◽  
Vol 11 (3) ◽  
pp. 199-206 ◽  
Author(s):  
Anzhi Wang ◽  
Minghui Wang ◽  
Gang Pan ◽  
Xiaoyan Yuan
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Bayesian Fusion ◽  
High Level

scholarly journals Salient Object Detection Based on Background Feature Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Kan Huang ◽  
Yong Zhang ◽  
Bo Lv ◽  
Yongbiao Shi
Keyword(s):  
Object Detection ◽  
Clustering Algorithm ◽  
Geodesic Distance ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Salient Object ◽  
Edge Preserving ◽  
Feature Clustering ◽  
Background Distribution ◽  
Extensive Evaluation

Automatic estimation of salient object without any prior knowledge tends to greatly enhance many computer vision tasks. This paper proposes a novel bottom-up based framework for salient object detection by first modeling background and then separating salient objects from background. We model the background distribution based on feature clustering algorithm, which allows for fully exploiting statistical and structural information of the background. Then a coarse saliency map is generated according to the background distribution. To be more discriminative, the coarse saliency map is enhanced by a two-step refinement which is composed of edge-preserving element-level filtering and upsampling based on geodesic distance. We provide an extensive evaluation and show that our proposed method performs favorably against other outstanding methods on two most commonly used datasets. Most importantly, the proposed approach is demonstrated to be more effective in highlighting the salient object uniformly and robust to background noise.

scholarly journals Salient Object Detection with Semantic Priors

2017 ◽  
Author(s):  
Tam V. Nguyen ◽  
Luoqi Liu
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Salient Object ◽  
Artificial Intelligence Research ◽  
Semantic Map ◽  
Regional Features ◽  
Computational Sciences

Salient object detection has increasingly become a popular topic in cognitive and computational sciences, including computer vision and artificial intelligence research. In this paper, we propose integrating semantic priors into the salient object detection process. Our algorithm consists of three basic steps. Firstly, the explicit saliency map is obtained based on the semantic segmentation refined by the explicit saliency priors learned from the data. Next, the implicit saliency map is computed based on a trained model which maps the implicit saliency priors embedded into regional features with the saliency values. Finally, the explicit semantic map and the implicit map are adaptively fused to form a pixel-accurate saliency map which uniformly covers the objects of interest. We further evaluate the proposed framework on two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental results demonstrate that our method outperforms other state-of-the-art methods.

Salient Object Detection by Spatiotemporal and Semantic Features in Real-Time Video Processing Systems

2020 ◽  
Vol 67 (11) ◽  
pp. 9893-9903
Author(s):  
Yuming Fang ◽  
Guanqun Ding ◽  
Wenying Wen ◽  
Feiniu Yuan ◽  
Yong Yang ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Video Processing ◽  
Salient Object Detection ◽  
Semantic Features ◽  
Salient Object

scholarly journals Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2163
Author(s):  
Zhou Huang ◽  
Huaixin Chen ◽  
Biyuan Liu ◽  
Zhixi Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Network Architecture ◽  
Salient Object Detection ◽  
Optical Remote Sensing ◽  
Salient Object ◽  
Remote Sensing Images ◽  
Multi Level ◽  
Object Area ◽  
High Level

Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.

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