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RGB-D Salient Object Detection

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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.

<span style="color:grey; opacity: 0.6">( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )</span>

논문

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RGB-D Salient Object Detection

논문

Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective

Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection

MambaSOD: Dual Mamba-Driven Cross-Modal Fusion Network for RGB-D Salient Object Detection

CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection

A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network

DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion

Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection

HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection

RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection

Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection

Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection

HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness

CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection

SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection

SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification

TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection

Dynamic Message Propagation Network for RGB-D Salient Object Detection

Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection

Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection

An Energy-Based Prior for Generative Saliency

GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection

Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction

Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction

CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection

MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention

M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection

ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection

RGB-D Salient Object Detection with Ubiquitous Target Awareness

Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection

TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network

Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection