TY  - JOUR
AU  - Khan, Abdurrahman
AU  - Shah, Hasnain Ali
PY  - 2026
DA  - 2026/02/14
TI  - Context Refinement with Multi-Attention Fusion for Saliency Segmentation Using Depth-Aware RGBD Sensing
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 3
IS  - 1
SP  - 27
EP  - 38
DO  - 10.62762/TSCC.2025.587957
UR  - https://www.icck.org/article/abs/TSCC.2025.587957
KW  - RGB-D saliency
KW  - attention mechanisms
KW  - multi-modal fusion
KW  - depth refinement
KW  - contextual features
AB  - Salient object detection in RGB-D imagery remains challenging due to inconsistent depth quality and suboptimal cross-modal fusion strategies. This paper presents a novel dual-stream architecture that integrates contextual feature refinement with adaptive attention mechanisms for robust RGB-D saliency detection. We extract two features from the ResNet-50 backbone for both the RGB and depth streams, capturing low-level spatial details and high-level semantic representations. We introduce a Contextual Feature Refinement Module (CFRM) that captures multi-scale dependencies through parallel dilated convolutions, enabling hierarchical context aggregation without substantial computational overhead. To enhance discriminative feature learning, we employ channel attention for inter-channel recalibration and a modified spatial attention mechanism utilizing quadruple feature statistics for precise localization. Recognizing that existing depth maps in benchmark datasets are outdated and degraded in quality, we introduce refined depth maps generated with Depth Anything V2, which significantly improve cross-modal alignment and detection performance. The progressive fusion strategy integrates complementary RGB and depth information across semantic hierarchies, while the saliency prediction block generates high-resolution predictions via gradual spatial expansion. Extensive experiments across six benchmark datasets validate our approach, achieving competitive performance with recent state-of-the-art methods.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Khan2026Context,
  author = {Abdurrahman Khan and Hasnain Ali Shah},
  title = {Context Refinement with Multi-Attention Fusion for Saliency Segmentation Using Depth-Aware RGBD Sensing},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {27-38},
  doi = {10.62762/TSCC.2025.587957},
  url = {https://www.icck.org/article/abs/TSCC.2025.587957},
  abstract = {Salient object detection in RGB-D imagery remains challenging due to inconsistent depth quality and suboptimal cross-modal fusion strategies. This paper presents a novel dual-stream architecture that integrates contextual feature refinement with adaptive attention mechanisms for robust RGB-D saliency detection. We extract two features from the ResNet-50 backbone for both the RGB and depth streams, capturing low-level spatial details and high-level semantic representations. We introduce a Contextual Feature Refinement Module (CFRM) that captures multi-scale dependencies through parallel dilated convolutions, enabling hierarchical context aggregation without substantial computational overhead. To enhance discriminative feature learning, we employ channel attention for inter-channel recalibration and a modified spatial attention mechanism utilizing quadruple feature statistics for precise localization. Recognizing that existing depth maps in benchmark datasets are outdated and degraded in quality, we introduce refined depth maps generated with Depth Anything V2, which significantly improve cross-modal alignment and detection performance. The progressive fusion strategy integrates complementary RGB and depth information across semantic hierarchies, while the saliency prediction block generates high-resolution predictions via gradual spatial expansion. Extensive experiments across six benchmark datasets validate our approach, achieving competitive performance with recent state-of-the-art methods.},
  keywords = {RGB-D saliency, attention mechanisms, multi-modal fusion, depth refinement, contextual features},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}