mlsanityData to 7 Oct 2026

RGB-D Salient Object Detection with Ubiquitous Target Awareness

arXiv 2109.03425 ↗First posted 8 Sep 2021cs.CVcited by 12

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Abstract (arXiv)

Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 39 of its 76 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

39 of 76 references.

References · 39

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27 more
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  25. 2020A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Imagescited by 19
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Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 6; 2024: 1.

12 papers.

  1. 2023DFormer: Rethinking RGBD Representation Learning for Semantic Segmentationcited by 58
  2. 2024GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformercited by 42
  3. 2023HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awarenesscited by 31
  4. 2023HRTransNet: HRFormer-Driven Two-Modality Salient Object Detectioncited by 27
  5. 2022Glass Segmentation with RGB-Thermal Image Pairscited by 26
  6. 2023Towards Diverse Binary Segmentation via A Simple yet General Gated Networkcited by 17
  7. 2023Object Segmentation by Mining Cross-Modal Semanticscited by 12
  8. 2022Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detectioncited by 3
  9. 2022Mirror Complementary Transformer Network for RGB-thermal Salient Object Detectioncited by 3
  10. 2023Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessmentcited by 3
  11. 2022Dynamic Message Propagation Network for RGB-D Salient Object Detectioncited by 0
  12. 2022Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detectioncited by 0