https://gitee.com/Lin-Qinwei/CIR-Net-MindSpore.git
공식 코드CIR-Net: Cross-modality Interaction and Refinement for 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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Cross-Modal Weighting Network for RGB-D Salient Object Detection
Accurate RGB-D Salient Object Detection via Collaborative Learning
RGB-D Salient Object Detection via 3D Convolutional Neural Networks
A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection
TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection
TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network
Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention
CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.