BASNet: Boundary-Aware Salient Object Detection

Qin, Xuebin; Zhang, Zichen; Huang, Chenyang; Gao, Chao; Dehghan, Masood; Jägersand, Martin

doi:10.1109/cvpr.2019.00766

Cited by 1,102 publications

(811 citation statements)

References 56 publications

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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%

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CAGNet: Content-Aware Guidance for Salient Object Detection

Mohammadi

Noori

Bahri

et al. 2020

Pattern Recognition

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

confidence: 99%

“…In the second row, the appearance of the feet of the doll is different from the rest of the doll. While both scenarios have caused confusion for two recent methods (BAS-Net [15] and SRM [16]), our method (denoted as CAGNet-R) is capable of handling these complicated scenarios and generating a more accurate prediction.…”

Section: Introductionmentioning

confidence: 99%

CAGNet: Content-Aware Guidance for Salient Object Detection

Mohammadi

Noori

Bahri

et al. 2020

Pattern Recognition

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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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“…Most recently, a large number of edge information enhancement methods have been proposed [26][27][28][29][30]. Zhao et al [26] proposed to use the complementarity of edge information and saliency information to enhance the boundary and location information of saliency objects.…”

Section: Feature Concatenate and Dense Supervision Refinementmentioning

confidence: 99%

“…We compare the effectiveness of the DMS model with 16 saliency methods, including 12 traditional saliency algorithms (ITTI [40], LC [41], SR [42], AC [43], FT [44], MSS [45], PHOT [46], HC [47], RC [47], SF [48], BMS [49], and MBP [50]), and 8 deep learning methods (U-Net [51], FCN [19], R3Net [24], DSS [33], PiCANet [52], BASNet [29], PoolNet [53], and EGNet [26]). We implement the traditional saliency algorithm through the toolbox provided in [16].…”

Section: Model Comparisonmentioning

confidence: 99%

Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

Yan

Song

Liu

2020

Mathematical Problems in Engineering

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In recent years, researches in the field of salient object detection have been widely made in many industrial visual inspection tasks. Automated surface inspection (ASI) can be regarded as one of the most challenging tasks in computer vision because of its high cost of data acquisition, serious imbalance of test samples, and high real-time requirement. Inspired by the requirements of industrial ASI and the methods of salient object detection (SOD), a task mode of defect type classification plus defect area segmentation and a novel deeper and mixed supervision network (DMS) architecture is proposed. The backbone network ResNeXt-101 was pretrained on ImageNet. Firstly, we extract five multiscale feature maps from backbone and concatenate them layer by layer. In addition, to obtain the classification prediction and saliency maps in one stage, the image-level and pixel-level ground truth is trained in a same side output network. Supervision signal is imposed on each side layer to realize deeper and mixed training for the network. Furthermore, the DMS network is equipped with residual refinement mechanism to refine the saliency maps of input images. We evaluate the DMS network on 4 open access ASI datasets and compare it with other 20 methods, which indicates that mixed supervision can significantly improve the accuracy of saliency segmentation. Experiment results show that the proposed method can achieve the state-of-the-art performance.

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BASNet: Boundary-Aware Salient Object Detection

Cited by 1,102 publications

References 56 publications

CAGNet: Content-Aware Guidance for Salient Object Detection

CAGNet: Content-Aware Guidance for Salient Object Detection

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

Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

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