Learning Uncertain Convolutional Features for Accurate Saliency Detection

Zhang, Pingping; Wang, Dong; Lu, Huchuan; Wang, Hongyu; Yin, Baocai

doi:10.1109/iccv.2017.32

Cited by 434 publications

(286 citation statements)

References 46 publications

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“…We compare our algorithm with 9 state-of-the-art methods, including RFCN [34], DHS [24], UCF [50], Amulet [49], NLDF [26], DSS [14], RAS [3], DGF [38] and DGRL [36]. For a fair comparison, we use either the implementations with recommended parameter settings or the saliency maps provided by the authors.…”

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

confidence: 99%

Towards High-Resolution Salient Object Detection

Zeng

Zhang

Lin

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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174

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Deep neural network based methods have made a significant breakthrough in salient object detection. However, they are typically limited to input images with low resolutions (400 × 400 pixels or less). Little effort has been made to train deep neural networks to directly handle salient object detection in very high-resolution images. This paper pushes forward high-resolution saliency detection, and contributes a new dataset, named High-Resolution Salient Object Detection (HRSOD). To our best knowledge, HRSOD is the first high-resolution saliency detection dataset to date. As another contribution, we also propose a novel approach, which incorporates both global semantic information and local high-resolution details, to address this challenging task. More specifically, our approach consists of a Global Semantic Network (GSN), a Local Refinement Network (LRN) and a Global-Local Fusion Network (GLFN). GSN extracts the global semantic information based on down-sampled entire image. Guided by the results of GSN, LRN focuses on some local regions and progressively produces high-resolution predictions. GLFN is further proposed to enforce spatial consistency and boost performance. Experiments illustrate that our method outperforms existing state-of-the-art methods on high-resolution saliency datasets by a large margin, and achieves comparable or even better performance than them on widely-used saliency benchmarks. The HRSOD dataset is available at https://github.com/yi94code/HRSOD.

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

confidence: 99%

Towards High-Resolution Salient Object Detection

Zeng

Zhang

Lin

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

174

View full text Add to dashboard Cite

show abstract

“…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

109

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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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Learning Uncertain Convolutional Features for Accurate Saliency Detection

Cited by 434 publications

References 46 publications

Towards High-Resolution Salient Object Detection

Towards High-Resolution Salient Object Detection

CAGNet: Content-Aware Guidance for Salient Object Detection

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

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