scholarly journals Hybrid-Attention Network for RGB-D Salient Object Detection

2020 ◽  
Vol 10 (17) ◽  
pp. 5806 ◽  
Author(s):  
Yuzhen Chen ◽  
Wujie Zhou
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Depth Information ◽  
Salient Object ◽  
Depth Sensor ◽  
Attention Network ◽  
Depth Maps ◽  
Saliency Prediction ◽  
Ablation Study ◽  
High Level

Depth information has been widely used to improve RGB-D salient object detection by extracting attention maps to determine the position information of objects in an image. However, non-salient objects may be close to the depth sensor and present high pixel intensities in the depth maps. This situation in depth maps inevitably leads to erroneously emphasize non-salient areas and may have a negative impact on the saliency results. To mitigate this problem, we propose a hybrid attention neural network that fuses middle- and high-level RGB features with depth features to generate a hybrid attention map to remove background information. The proposed network extracts multilevel features from RGB images using the Res2Net architecture and then integrates high-level features from depth maps using the Inception-v4-ResNet2 architecture. The mixed high-level RGB features and depth features generate the hybrid attention map, which is then multiplied to the low-level RGB features. After decoding by several convolutions and upsampling, we obtain the final saliency prediction, achieving state-of-the-art performance on the NJUD and NLPR datasets. Moreover, the proposed network has good generalization ability compared with other methods. An ablation study demonstrates that the proposed network effectively performs saliency prediction even when non-salient objects interfere detection. In fact, after removing the branch with high-level RGB features, the RGB attention map that guides the network for saliency prediction is lost, and all the performance measures decline. The resulting prediction map from the ablation study shows the effect of non-salient objects close to the depth sensor. This effect is not present when using the complete hybrid attention network. Therefore, RGB information can correct and supplement depth information, and the corresponding hybrid attention map is more robust than using a conventional attention map constructed only with depth information.

Global contextual guided residual attention network for salient object detection

2021 ◽  
Author(s):  
Jun Wang ◽  
Zhengyun Zhao ◽  
Shangqin Yang ◽  
Xiuli Chai ◽  
Wanjun Zhang ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Attention Network

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 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

Hierarchical U-Shape Attention Network for Salient Object Detection

2020 ◽  
Vol 29 ◽  
pp. 8417-8428 ◽  
Author(s):  
Sanping Zhou ◽  
Jinjun Wang ◽  
Jimuyang Zhang ◽  
Le Wang ◽  
Dong Huang ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Attention Network

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.

scholarly journals RGB-D salient object detection: A survey

2021 ◽  
Author(s):  
Tao Zhou ◽  
Deng-Ping Fan ◽  
Ming-Ming Cheng ◽  
Jianbing Shen ◽  
Ling Shao
Keyword(s):  
Object Detection ◽  
Spatial Information ◽  
Salient Object Detection ◽  
Future Research ◽  
Salient Object ◽  
Human Visual Perception ◽  
Depth Sensors ◽  
Depth Maps ◽  
Benchmark Datasets ◽  
Comprehensive Survey

AbstractSalient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

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