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RGB-D Salient Object Detection

RGB-D Salient object detection (SOD) aims at distinguishing the most visually distinctive objects or regions in a scene from the given RGB and Depth data. It has a wide range of applications, including video/image segmentation, object recognition, visual tracking, foreground maps evaluation, image retrieval, content-aware image editing, information discovery, photosynthesis, and weakly supervised semantic segmentation. Here, depth information plays an important complementary role in finding salient objects. Online benchmark: http://dpfan.net/d3netbenchmark.

( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )

Papers

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Showing 21–30 of 88 papers

TitleStatusHype
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency DetectionCode1
An Energy-Based Prior for Generative SaliencyCode1
GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection—0
Joint Learning of Salient Object Detection, Depth Estimation and Contour ExtractionCode1
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection—0
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB ImagesCode0
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction—0
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object DetectionCode1
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion AttentionCode1
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object DetectionCode0
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