Cross-Modal Weighting Network for RGB-D Salient Object Detection

Li, Gongyang; Li, Zhi; Ye, Linwei; Wang, Yang; Ling, Haibin

doi:10.1007/978-3-030-58520-4_39

Cited by 138 publications

(72 citation statements)

References 31 publications

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“…In order to prove the reliability of the whole model, this paper refers to [4] [5][6] [7] and uses five data sets, namely NLPR, SIP, DUT-RGBD, STERE and RGBD135. In this paper, the model is compared with advanced models at the present stage that are also based on RGB-D for significance detection, including CPFP [7] ,CMW [8] , D3NET [5] , ICNET [9] , DCMF [10] . At the same time, four evaluation indicators were used, and they were S-measure,F-measure, MAE,avgF.…”

Section: The Experimental Resultsmentioning

confidence: 99%

Multi-level interactions for RGB-D object detection

Dai

Zhang

2022

J. Phys.: Conf. Ser.

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In order to efficiently utilize high-level information and depth information in RGB-D saliency object detection, multi-level fusion is studied. Different from existing methods which ignore feature dilution in the process of feature downward transmission, a multi-level interactive fusion method is designed and compared with five advanced models through four evaluation indexes. The experimental results show that the model in this paper is advanced.

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Section: The Experimental Resultsmentioning

confidence: 99%

Multi-level interactions for RGB-D object detection

Dai

Zhang

2022

J. Phys.: Conf. Ser.

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Section: The Experimental Resultsmentioning

confidence: 99%

“…We tested this model on five widely used RGBD data sets [6,7,8,9], namely NLPR, SIP, DUT-RGBD, STERE and RGBD135. In order to prove the effectiveness of this method, compare it with Five advanced models were compared, including CPFP [2], CMW [9], D3NET [7], ICNET [10], DCMF [11]. Four evaluation indicators were used for evaluation, namely maximum F-measure (Fmaxβ), S-measure (Sα), maximum E-measure (Emax ξ) and mean absolute error (MAE).…”

Section: The Experimental Resultsmentioning

confidence: 99%

RGBD salient object detection based on depth feature enhancement

Shi

Guan

2022

J. Phys.: Conf. Ser.

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The depth map contains spatial location information, which has been proven to be beneficial for RGBD salient object detection. However, the quality of the depth map will directly affect the accuracy of RGBD saliency detection. In order to solve the problem of different quality of depth maps, a detection model based on depth enhancement is designed to enhance the available depth information and filter low-quality depth maps. By comparing four evaluation indicators with five advanced models, the experimental results show that the model in this paper is advanced.

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“…To effectively explore the correlations between RGB images and depth maps, several methods propose a multi-scale fusion strategy [42,43,55,109,116,122,123,128]. These models can be divided into two categories.…”

Section: Multi-scale Fusionmentioning

confidence: 99%

RGB-D salient object detection: A survey

et al. 2021

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Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

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Cross-Modal Weighting Network for RGB-D Salient Object Detection

Cited by 138 publications

References 31 publications

Multi-level interactions for RGB-D object detection

Multi-level interactions for RGB-D object detection

RGBD salient object detection based on depth feature enhancement

RGB-D salient object detection: A survey

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