Bi-directional Progressive Guidance Network for RGB-D Salient Object Detection

Yang Yang, Qi Qin, Yongjiang Luo, Yi Liu, Qiang Zhang, Jungong Han

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

21 Dyfyniadau(SciVal)
203 Wedi eu Llwytho i Lawr (Pure)


Most existing RGB-D salient detection models pay more attention to the quality of the depth images, while in some special cases, the quality of RGB images may even have greater impacts on saliency detection, which has long been ignored and underestimated. To address this problem, in this paper, we present a Bi-directional Progressive Guidance Network (BPGNet) for RGB-D salient object detection, where the qualities of both RGB and depth images are involved. Since it is usually difficult to determine which modality data have low quality in advance, a bi-directional framework based on progressive guidance (PG) strategy is employed to extract and enhance the unimodal features with the aid of another modality data via the alternative interactions between the saliency prediction results and the extracted features from the multi-modality input data. Specifically, the proposed PG strategy is achieved by using the proposed Global Context Awareness (GCA), Auxiliary Feature Extraction (AFE) and Cross-modality Feature Enhancement (CFE) modules. Benefiting from the proposed PG strategy, the disturbing information within the input RGB and depth images can be well suppressed, while the discriminative information within the input images gets enhanced. On top of that, a Fusion Prediction Module (FPM) is further designed to adaptively select those features with higher discriminability as well as enhancing the common information for the final saliency prediction. Experimental results demonstrate that our proposed model is comparable to those of state-of-the-art RGB-D SOD models.
Iaith wreiddiolSaesneg
Tudalennau (o-i)5346-5360
Nifer y tudalennau15
CyfnodolynIEEE Transactions on Circuits and Systems for Video Technology
Rhif cyhoeddi8
Dyddiad ar-lein cynnar19 Ion 2022
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Awst 2022

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