Salient object detection via boosting object-level distinctiveness and saliency refinement

Yan, Xiaoyun; Wang, Yuehuan; Song, Qiong; Dai, Kaiheng

doi:10.1016/j.jvcir.2017.06.013

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…Most importantly, the advantages of the proposed n-sigmoid CSA-VoteNet stems from two significant facts: on the one hand, it assigns different relevance weights to different elements of the input data during the voting process; on the other hand, the attention map enables the network to emphasize important features and suppress irrelevant or noisy ones. COG [34] 2D-driven [35] F-PointNet [36] VoteNet [12] MLCVNet [37] DeMF [38] CSA-VoteNet A-SCN [39] Point-attention [40] CAA [31] Point-transformer [32] Offset-attention [41] CSA A-SCN [39] Point-attention [40] CAA [31] Pointtransformer [32] Offset-attention [41] CSA…”

Section: Methods In Comparisonmentioning

confidence: 99%

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An Innovative n-Shifted Sigmoid Channel and Spatial Attention Module for Efficient 3D Scene Object Detection

Du,

Burume,

Liu

2024

Preprint

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Recently, attention mechanisms have developed into an important tool for performance improvement of deep neural networks. In computer vision, attention mechanisms are generally divided into two main branches: spatial and channel attention. Both attention categories have their own advantages. The fusion of both attentions achieves higher performance, on the cost of the computational load. This paper introduces an innovative and lighter n-shifted sigmoid channel and spatial attention (CSA) module to reduce the computational cost and to improve the 3D scene relevant features selection. To validate the proposed attention module, 3D scene object detection in the deep Hough voting point sets is considered as the testing application. The proposed attention module with its piecewise n-shifted sigmoid activation function improves the network’s learning and generalization capacity which effectively predict bounding box parameters directly from 3D scenes and detect objects more accurately. This advantage is achieved by selectively attending to more relevant features of the input data. When used in the deep Hough voting point sets, the proposed attention module outperforms state-of-the-art 3D detection methods on the sizable SUNRGBD dataset. Experiments conducted showed an increase of 12.02 mean accuracy precision (mAP) when compared to the celebrated VoteNet (without attention). It also got 9.92 mAP higher compared to the MLVCNet, and 10.32 mAP higher than the Point Transformer. The proposed model not only decreases the sigmoid vanishing gradient problem but also brings out valuable features by fusing channel-wise and spatial information while improving accuracy results in 3D object detection.

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Section: Methods In Comparisonmentioning

confidence: 99%

“…H3DNet [43] LGR-Net [44] HGNet [45] SPOT [46] Feng [47] MLCVNey [37] VENet [46] DeMF [38] CAGroup3D [49] TR3D+FF [50] Point-GCC+TR3D+FF [52]…”

Section: Methods Without Attentionmentioning

confidence: 99%

An Innovative n-Shifted Sigmoid Channel and Spatial Attention Module for Efficient 3D Scene Object Detection

Du,

Burume,

Liu

2024

Preprint

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“…Figure 17 shows the estimated salient region produced by our method is inaccurate. To overcome this limitation, some studies have been conducted by incorporating more features such as texture [48] or even high-level knowledge [49]. We will work on these problems in the future.…”

Section: Failure Casesmentioning

confidence: 99%

Salient Region Detection Using Diffusion Process with Nonlocal Connections

et al. 2018

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Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.

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“…The AdaBoost algorithm is an algorithm that can upgrade a weak learner to a strong learner [13][14] . The basic idea of the adaboost algorithm is to learn a weak learner from the training sample, then change the weight of the sample according to the learning result, then learn a weak learner according to the modified training sample, and continue to do it repeatedly until the number of the learners reaches the preset value, in the T A weak learner is weighted to get a strong learner.…”

Section: Strong Classifier Learning Based On Adaboost Algorithmmentioning

confidence: 99%

Hemolysis detection based on SVM of Adaboost classification algorithm

Shi

Wang

Zhao³

2018

MATEC Web Conf.

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Aiming at the problem that clinical hemolysis is difficult to be observed and judged, a method of Adaboost learning classification based on SVM is proposed. The method firstly extracts the basic features of the target area of the blood sample, such as the average of the gray level, the standard deviation of the gray level and the appearance frequency of the particles, as the input eigenvectors of the learning, and carries out SVM weak learner learning. Subsequently, Adaboost algorithm is used to measure the weak learner Set linear weighting, so as to enhance the strong learning device; Finally, online testing, calculation of test sample hemolytic degree and classification. The Adaboost learning classification test based on SVM is compared with the macroscopic and red blood cell counting methods. The experimental results show that the learning-based classification testing method achieves higher detection accuracy without subjective factors and has the highest detection efficiency.

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Salient object detection via boosting object-level distinctiveness and saliency refinement

Cited by 9 publications

References 45 publications

An Innovative n-Shifted Sigmoid Channel and Spatial Attention Module for Efficient 3D Scene Object Detection

An Innovative n-Shifted Sigmoid Channel and Spatial Attention Module for Efficient 3D Scene Object Detection

Salient Region Detection Using Diffusion Process with Nonlocal Connections

Hemolysis detection based on SVM of Adaboost classification algorithm

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