Papers RGB-D Salient Object Detection
“RGB-D Salient Object Detection” 태그가 달린 논문 91편 · 필터 해제
Weakly-Supervised RGB-D Salient Object Detection via SAM-driven Pseudo Annotation and State Space Interaction-based Diffusion
Weakly-supervised RGB-D Salient Object Detection (SOD) is explored to reduce the heavy burden of pixel-level annotations. But scribble annotations lack the structure and details of objects, resulting in inaccurate salien…
RGB-D Salient Object DetectionSTENet: Superpixel Token Enhancing Network for RGB-D Salient Object Detection
Transformer-based methods for RGB-D Salient Object Detection (SOD) have gained significant interest, owing to the transformer's exceptional capacity to capture long-range pixel dependencies. Nevertheless, current RGB-D S…
RGB-D Salient Object DetectionLEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detection
RGB-D salient object detection (SOD) aims to identify the most conspicuous objects in a scene with the incorporation of depth cues. Existing methods mainly rely on CNNs, limited by the local receptive fields, or Vision T…
RGB-D Salient Object DetectionComputational EfficiencyLightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective
Current RGB-D methods usually leverage large-scale backbones to improve accuracy but sacrifice efficiency. Meanwhile, several existing lightweight methods are difficult to achieve high-precision performance. To balance t…
object-detectionObject DetectionRGB-D Salient Object DetectionSalient Object DetectionDual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection
RGB-D salient object detection (SOD), aiming to highlight prominent regions of a given scene by jointly modeling RGB and depth information, is one of the challenging pixel-level prediction tasks. Recently, the dual-atten…
object-detectionObject DetectionRGB-D Salient Object DetectionSalient Object DetectionMambaSOD: Dual Mamba-Driven Cross-Modal Fusion Network for RGB-D Salient Object Detection
The purpose of RGB-D Salient Object Detection (SOD) is to pinpoint the most visually conspicuous areas within images accurately. While conventional deep models heavily rely on CNN extractors and overlook the long-range c…
Mambaobject-detectionObject DetectionRGB-D Salient Object Detection+1CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
The depth/thermal information is beneficial for detecting salient object with conventional RGB images. However, in dual-modal salient object detection (SOD) model, the robustness against noisy inputs and modality missing…
CoLALanguage ModelingLanguage Modellingobject-detection+5A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network
Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales alwaysresults in large model sizes, impeding the app…
object-detectionObject DetectionRGB-D Salient Object DetectionSaliency Detection+1DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous works that encode RGB-D information with…
3D geometryDecoderobject-detectionObject Detection+6Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion
RGB-guided depth completion aims at predicting dense depth maps from sparse depth measurements and corresponding RGB images, where how to effectively and efficiently exploit the multi-modal information is a key issue. Gu…
Depth Completionobject-detectionObject DetectionRGB-D Salient Object Detection+1Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved. In recent years, the important role of Convolutiona…
object-detectionObject DetectionRGB-D Salient Object DetectionSalient Object DetectionHODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection
RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the…
object-detectionObject DetectionRGB-D Salient Object DetectionSalient Object DetectionRXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB input and another type of data input to reso…
DecoderImage ManipulationImage Manipulation DetectionObject+4Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection
In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter…
Objectobject-detectionObject DetectionPrediction+3Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection
Most of existing RGB-D salient object detection (SOD) methods follow the CNN-based paradigm, which is unable to model long-range dependencies across space and modalities due to the natural locality of CNNs. Here we propo…
object-detectionObject DetectionRGB-D Salient Object DetectionSalient Object DetectionHiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained detail…
Decoderobject-detectionObject DetectionRGB-D Salient Object Detection+2CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the …
Decoderobject-detectionObject DetectionRGB-D Salient Object Detection+1Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection
Recently CNN-based RGB-D salient object detection (SOD) has obtained significant improvement on detection accuracy. However, existing models often fail to perform well in terms of efficiency and accuracy simultaneously. …
CPUobject-detectionObject DetectionRGB-D Salient Object Detection+2SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is …
object-detectionObject DetectionRGB-D Salient Object DetectionSalient Object Detection+1SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification
RGB-D SOD uses depth information to handle challenging scenes and obtain high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection methods overwhelmingly rely on the strategy of directly fusing depth…
image-classificationImage ClassificationMisinformationobject-detection+5