Visual Saliency Detection Based on Full Convolution Neural Networks and Center Prior

Jian, Muwei; Wang, Jiaojin; Liu, Xiangyu; Yu, Hui

doi:10.1109/hsi47298.2019.8942601

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…However, this strategy of saliency prediction based on five scales increases the training time and may also cause overfitting. Fan et al [25] employed the depth depurator unit to filter out low-quality depth maps with large MAE for saliency detection. Nevertheless, the threshold is a fixed hyperparameter, which cannot dynamically adjust its value to adaptively judge the quality of depth maps.…”

Section: Rgb-d Salient Object Detectionmentioning

confidence: 99%

“…Nevertheless, the threshold is a fixed hyperparameter, which cannot dynamically adjust its value to adaptively judge the quality of depth maps. Different from [25], Piao et al [18] proposed a Depth Distiller to convert the depth information to the RGB stream to guide salient prediction. Although this idea is compelling, the performance may be greatly limited by the quality of the predicted depth map.…”

Section: Rgb-d Salient Object Detectionmentioning

confidence: 99%

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A2TPNet: Alternate Steered Attention and Trapezoidal Pyramid Fusion Network for RGB-D Salient Object Detection

et al. 2022

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RGB-D salient object detection (SOD) aims at locating the most eye-catching object in visual input by fusing complementary information of RGB modality and depth modality. Most of the existing RGB-D SOD methods integrate multi-modal features to generate the saliency map indiscriminately, ignoring the ambiguity between different modalities. To better use multi-modal complementary information and alleviate the negative impact of ambiguity among different modalities, this paper proposes a novel Alternate Steered Attention and Trapezoidal Pyramid Fusion Network (A2TPNet) for RGB-D SOD composed of Cross-modal Alternate Fusion Module (CAFM) and Trapezoidal Pyramid Fusion Module (TPFM). CAFM is focused on fusing cross-modal features, taking full consideration of the ambiguity between cross-modal data by an Alternate Steered Attention (ASA), and it reduces the interference of redundant information and non-salient features in the interactive process through a collaboration mechanism containing channel attention and spatial attention. TPFM endows the RGB-D SOD model with more powerful feature expression capabilities by combining multi-scale features to enhance the expressive ability of contextual semantics of the model. Extensive experimental results on five publicly available datasets demonstrate that the proposed model consistently outperforms 17 state-of-the-art methods.

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Section: Rgb-d Salient Object Detectionmentioning

confidence: 99%

Section: Rgb-d Salient Object Detectionmentioning

confidence: 99%