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RGB-D Salient Object Detection

2D Object Detection › Object Detection › RGB-D Salient Object Detection 98 평가 기록 8 datasets

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>

RGB-D Salient Object Detection

논문

BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection

DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

Siamese Network for RGB-D Salient Object Detection and Beyond

Uncertainty Inspired RGB-D Saliency Detection

A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection

A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection

Accurate RGB-D Salient Object Detection via Collaborative Learning

Bilateral Attention Network for RGB-D Salient Object Detection

Cascade Graph Neural Networks for RGB-D Salient Object Detection

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

Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection

Cross-Modal Weighting Network for RGB-D Salient Object Detection

Depth Quality Aware Salient Object Detection

Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection

Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection

Is Depth Really Necessary for Salient Object Detection?

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

Learning Selective Self-Mutual Attention for RGB-D Saliency Detection

Local Background Enclosure for RGB-D Salient Object Detection

Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection

Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks

RGB-D Salient Object Detection with Cross-Modality Modulation and Selection

RGBD Salient Object Detection via Deep Fusion

Select, Supplement and Focus for RGB-D Saliency Detection

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

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders