Learning to Detect Salient Objects with Image-Level Supervision

Wang, Lijun; Lu, Huchuan; Wang, Yifan; Feng, Mengyang; Wang, Dong; Yin, Baocai; Ruan, Xiang

doi:10.1109/cvpr.2017.404

Cited by 914 publications

(680 citation statements)

References 43 publications

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“…Here for a fair comparison, we train our model on the VGG [43] and ResNet [16], respectively. It can be seen that our model performs favorably against the state-of-the-art methods under all evaluation metrics on all the compared datasets especially on the relative challenging dataset SOD [36,44] (2.9% and 1.7% improvements in F-measure and S-measure) and the largest dataset DUTS [46] (3.0% and 2.5%). Specifically, Compared with the current best approach [33], the average Fmeasure improvement on six datasets is 1.9%.…”

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

confidence: 92%

“…We train our model on DUTS [46] dataset followed by [33,49,59,63]. For a fair comparison, we use VGG [43] and ResNet [16] as backbone networks, respectively.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…ECSSD [56] PASCAL-S [30] DUT-O [57] HKU-IS [27] SOD [36,37] DUTS-TE [46] Table 2. Quantitative comparison including max F-measure, MAE, and S-measure over six widely used datasets.…”

Section: Datasets and Evaluation Metricmentioning

confidence: 99%

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EGNet: Edge Guidance Network for Salient Object Detection

Zhao

Liu

Fan

et al. 2019

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

834

545

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Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, most existing FCNs-based methods still suffer from coarse object boundaries. In this paper, to solve this problem, we focus on the complementarity between salient edge information and salient object information. Accordingly, we present an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network. In the first step, we extract the salient object features by a progressive fusion way. In the second step, we integrate the local edge information and global location information to obtain the salient edge features. Finally, to sufficiently leverage these complementary features, we couple the same salient edge features with salient object features at various resolutions. Benefiting from the rich edge information and location information in salient edge features, the fused features can help locate salient objects, especially their boundaries more accurately. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on six widely used datasets without any pre-processing and post-processing. The source code is available at

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

confidence: 92%

“…We train our model on DUTS [46] dataset followed by [33,49,59,63]. For a fair comparison, we use VGG [43] and ResNet [16] as backbone networks, respectively.…”

Section: Implementation Detailsmentioning

confidence: 99%

See 1 more Smart Citation

EGNet: Edge Guidance Network for Salient Object Detection

Zhao

Liu

Fan

et al. 2019

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

834

545

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show abstract

“…To our best knowledge, HRSOD is cur- Concretely, (c) is from HKU-IS [21]. (d) is from DUTS-Test [33]. (e) is from THUR [5].…”

Section: High-resolution Saliency Detection Datasetmentioning

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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147

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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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“…In this paper, we choose the train set of DUTS [39], DAVIS [30] and FBMS [2] as our training set. We evaluate video salient object detection methods on DAVIS, FBMS and ViSal [43] benchmark.…”

Section: Methodsmentioning

confidence: 99%

Motion Guided Attention for Video Salient Object Detection

Chen²,

Li³

et al. 2019

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

161

112

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Video salient object detection aims at discovering the most visually distinctive objects in a video. How to effectively take object motion into consideration during video salient object detection is a critical issue. Existing stateof-the-art methods either do not explicitly model and harvest motion cues or ignore spatial contexts within optical flow images. In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images. We further introduce a series of novel motion guided attention modules, which utilize the motion saliency subnetwork to attend and enhance the sub-network for still images. These two sub-networks learn to adapt to each other by end-to-end training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks. We hope our simple and effective approach will serve as a solid baseline and help ease future research in video salient object detection. Code and models will be made available.

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Learning to Detect Salient Objects with Image-Level Supervision

Cited by 914 publications

References 43 publications

EGNet: Edge Guidance Network for Salient Object Detection

EGNet: Edge Guidance Network for Salient Object Detection

Towards High-Resolution Salient Object Detection

Motion Guided Attention for Video Salient Object Detection

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