Cross-Modal Weakly Supervised RGB-D Salient Object Detection with a Focus on Filamentary Structures DOI Creative Commons

Yifan Ding,

Weiwei Chen,

Guomin Zhang

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(10), P. 2990 - 2990

Published: May 9, 2025

Current weakly supervised salient object detection (SOD) methods for RGB-D images mostly rely on image-level labels and sparse annotations, which makes it difficult to completely contour boundaries in complex scenes, especially when detecting objects with filamentary structures. To address the aforementioned issues, we propose a novel cross-modal SOD framework. The framework can adequately exploit advantages of weak generate high-quality pseudo-labels, fully couple multi-scale features RGB depth precise saliency prediction. mainly consists pseudo-label generation network (CPGN) an asymmetric salient-region prediction (ASPN). Among them, CPGN is proposed sufficiently leverage pixel-level guidance provided by point enhanced semantic supervision text are used supervise subsequent training ASPN. better capture contextual information geometric from images, ASPN, asymmetrically progressive network, gradually extract using Swin-Transformer CNN encoders, respectively. This significantly enhances model’s ability perceive detailed Additionally, edge constraint module (ECM) designed sharpen edges predicted regions. experimental results demonstrate that method shows performance depicting objects, structures, than other methods.

Language: Английский

Multimodal medical image fusion based on dilated convolution and attention-based graph convolutional network DOI
Kaixin Jin, Xiwen Wang, Lifang Wang

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 124, P. 110359 - 110359

Published: May 1, 2025

Language: Английский

Citations

0
Cross-Modal Weakly Supervised RGB-D Salient Object Detection with a Focus on Filamentary Structures DOI Creative Commons

Yifan Ding,

Weiwei Chen,

Guomin Zhang

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(10), P. 2990 - 2990

Published: May 9, 2025

Current weakly supervised salient object detection (SOD) methods for RGB-D images mostly rely on image-level labels and sparse annotations, which makes it difficult to completely contour boundaries in complex scenes, especially when detecting objects with filamentary structures. To address the aforementioned issues, we propose a novel cross-modal SOD framework. The framework can adequately exploit advantages of weak generate high-quality pseudo-labels, fully couple multi-scale features RGB depth precise saliency prediction. mainly consists pseudo-label generation network (CPGN) an asymmetric salient-region prediction (ASPN). Among them, CPGN is proposed sufficiently leverage pixel-level guidance provided by point enhanced semantic supervision text are used supervise subsequent training ASPN. better capture contextual information geometric from images, ASPN, asymmetrically progressive network, gradually extract using Swin-Transformer CNN encoders, respectively. This significantly enhances model’s ability perceive detailed Additionally, edge constraint module (ECM) designed sharpen edges predicted regions. experimental results demonstrate that method shows performance depicting objects, structures, than other methods.

Language: Английский

Citations

0