RGB-D Salient Object Detection
56 papers with code • 8 benchmarks • 5 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.
( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )
Benchmarks
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Libraries
Use these libraries to find RGB-D Salient Object Detection models and implementations
NLPR
LFSD
SIP
NJU2K
ReDWeb-S
Latest papers with no code
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
The extensive research leveraging RGB-D information has been exploited in salient object detection.
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
In this paper, we take a step further by proposing a novel generative vision transformer with latent variables following an informative energy-based prior for salient object detection.
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention
In this work, we re-think Transformer and extend it to MutualFormer for multi-modality data representation.
M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection
The adjacent interactive aggregation module (AIAM) gradually integrates the neighbor features of high, middle and low levels.
ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection
Further, we proposed a type-based attention module (TAM) to optimize the network and enhance the multi-scale perception of different objects.
RGB-D Salient Object Detection with Ubiquitous Target Awareness
To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales.
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection
Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder.
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection
RGB-D salient object detection (SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data.
Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection
We further introduce a mask-guided refinement module(MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection.
Middle-level Fusion for Lightweight RGB-D Salient Object Detection
The former one first uses two sub-networks to extract unimodal features from RGB and depth images, respectively, and then fuses them for SOD.
