Attentive Feedback Network for Boundary-Aware Salient Object Detection

Feng, Mengyang; Lu, Huchuan; Ding, Errui

doi:10.1109/cvpr.2019.00172

Cited by 443 publications

(284 citation statements)

References 19 publications

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“…Recent deep-learning SOD models (MINet[ 161 ], SACNet[ 187 ], GateNet [ 166 ],

[ 193 ], LDF [ 148 ], DSRNet [ 164 ], EGNet [ 199 ], PoolNet [ 183 ], AFNet [ 177 ], MLMS [ 146 ], PAGE [ 44 ], CPD [ 173 ], BDPM [ 159 ], JDF [ 186 ], RAS [ 160 ], PAGR [ 180 ], C2S-Net [ 209 ], PiCANet [ 181 ], DSS [ 167 ], UCF [ 203 ], MSRNet [ 157 ], ILS [ 174 ], NLDF [ 15 ], AMULet [ 171 ], SCRN [ 162 ], BANet [ 194 ], BASNet [ 184 ], CapSal [ 147 ], DGRL [ 182 ], SRM [ 205 ]) are quantitatively evaluated using four evaluation metrics on five SOD datasets (DUTS-TE [ 174 ], DUT-OMRON [ 110 ], HKU-IS [ 154 ], ECSSD [ 103 ], Pascal-S [ 158 ]). The evaluation metrics used are maximum F-measure (

) [ 14 ], S-measure [ 224 ], E-measure [ 225 ], and mean average error (MAE) [ 106 ].…”

Section: Datasets Evaluation and Discussionmentioning

confidence: 99%

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Salient Object Detection Techniques in Computer Vision—A Survey

Gupta

Seal

Prasad

et al. 2020

Entropy

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Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

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“…Recent deep-learning SOD models (MINet[ 161 ], SACNet[ 187 ], GateNet [ 166 ],

) [ 14 ], S-measure [ 224 ], E-measure [ 225 ], and mean average error (MAE) [ 106 ].…”

Section: Datasets Evaluation and Discussionmentioning

confidence: 99%

“…In Reference [ 177 ], Feng et al performed two consecutive refinements of saliency features at every scale using the attentive feedback modules (AFM). Firstly, an initial coarse saliency map is computed to be fairly rich in spatial details along with semantics ( Figure 5 e).…”

Section: Deep Learning-based Salient Object Detectionmentioning

confidence: 99%

Salient Object Detection Techniques in Computer Vision—A Survey

Gupta

Seal

Prasad

et al. 2020

Entropy

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“…We compare our method with 16 previous state-of-the-art methods, namely MDF [28], RFCN [18], UCF [20], Amulet [13], NLDF [12], DSS [31], BMPM [21], PAGR [50], PiCANet [51], SRM [16], DGRL [32], MLMS [52], AFNet [53], CapSal [54], BASNet [15], and CPD [55]. For a fair comparison, we use the saliency maps provided by the authors.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

CAGNet: Content-Aware Guidance for Salient Object Detection

Mohammadi

Noori

Bahri

et al. 2020

Pattern Recognition

110

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Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results. However, it is still challenging to learn effective features for detecting salient objects in complicated scenarios, in which i) non-salient regions may have "salient-like" appearance; ii) the salient objects may have differentlooking regions. To handle these complex scenarios, we propose a Feature Guide Network which exploits the nature of low-level and high-level features to i) make foreground and background regions more distinct and suppress the non-salient regions which have "salient-like" appearance; ii) assign foreground label to different-looking salient regions.Furthermore, we utilize a Multi-scale Feature Extraction Module (MFEM) for each level of abstraction to obtain multi-scale contextual information. Finally, we design a loss function which outperforms the widely-used Cross-entropy loss. By adopting four different pre-trained models as the backbone, we prove that our method is very general with respect to the choice of the backbone model. Experiments on five challenging datasets demonstrate that our method achieves the state-of-the-art performance in terms of different evaluation metrics. Additionally, our approach contains fewer parameters than the existing ones, does not need any post-processing, and runs fast at a real-time speed of 28 FPS when processing a 480 × 480 image.

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“…We compare the proposed saliency detection method against previous 18 state-of-the-art methods, namely, MDF [13], RFCN [31], DHS [32], UCF [46], Amulet [34], NLDF [47], DSS [48], RAS [49], BMPM [33], PAGR [50], PiCANet [51], SRM [18], DGRL [17], MLMS [52], AFNet [53], CapSal [54], BASNet [55], and CPD [16]. We perform comparisons on five challenging datasets.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

DFNet: Discriminative feature extraction and integration network for salient object detection

Noori

Mohammadi

Majelan

et al. 2020

Engineering Applications of Artificial Intelligence

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Despite the powerful feature extraction capability of Convolutional Neural Networks, there are still some challenges in saliency detection. In this paper, we focus on two aspects of challenges: i) Since salient objects appear in various sizes, using single-scale convolution would not capture the right size. Moreover, using multi-scale convolutions without considering their importance may confuse the model. ii) Employing multilevel features helps the model use both local and global context. However, treating all features equally results in information redundancy. Therefore, there needs to be a mechanism to intelligently select which features in different levels are useful. To address the first challenge, we propose a Multi-scale Attention Guided Module. This module not only extracts multi-scale features effectively but also gives more attention to more discriminative feature maps corresponding to the scale of the salient object. To address the second challenge, we propose an Attention-based Multi-level Integrator Module to give the model the ability to assign different weights to multi-level feature maps. Furthermore, our Sharpening Loss function guides our network to output saliency maps with higher certainty and less blurry salient objects, and it has far better performance than the Cross-entropy loss. For the first time, we adopt four different backbones to show the generalization of our method. Experiments on five challenging datasets prove that our method achieves the state-of-the-art performance. Our approach is fast as well and can run at a real-time speed.

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Attentive Feedback Network for Boundary-Aware Salient Object Detection

Cited by 443 publications

References 19 publications

Salient Object Detection Techniques in Computer Vision—A Survey

Salient Object Detection Techniques in Computer Vision—A Survey

CAGNet: Content-Aware Guidance for Salient Object Detection

DFNet: Discriminative feature extraction and integration network for salient object detection

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