Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
Abstract
:1. Introduction
- A cross-modal adaptive interaction network (CMANet) is proposed to address the RGB-D SOD problem. This network integrates and enhances multi-scale features from both modalities, propagating them to subsequent layers while adaptively preserving crucial information at each level. Simultaneously capturing the global context, it preserves image details and spatial information, thereby yielding more accurate output results;
- We propose a cross-modal feature integration module (CMF), which mainly fuses information from different modalities (such as RGB and depth) to capture the complementary characteristics between the two modalities. Through this module, the model can simultaneously utilize the visual features such as color and texture of RGB images, as well as the spatial structure information of depth images, to generate more discernible and representative feature maps;
- We propose an adaptive feature fusion module (AFFM) that utilizes the intrinsic features of both modalities and adaptively integrates them with the outcomes from the previously shared network layer. This module merges features of different scales into the full-scale feature map, enhancing the model’s ability to perceive multi-scale information;
- Extensive experiments show that our cross-modal adaptive interaction network (CMANet) model outperforms 17 previous state-of-the-art methods on four public RGB-D SOD datasets.
2. Related Work
2.1. RGB Salient Object Detection
2.2. RGB-D Salient Object Detection
3. Methodology
3.1. Overview
3.2. Single-Modality Dedicated Network
3.3. Modal Integration Network
3.3.1. Cross-Modal Feature Integration Module
3.3.2. Adaptive Feature Fusion Module
3.4. Loss Function
4. Experimental Results and Analysis
4.1. Experimental Settings
4.1.1. Datasets
4.1.2. Evaluation Metrics
4.1.3. Experimental Details
4.2. Performance Comparison
4.2.1. Comparison with RGB-D SOD Models
4.2.2. Quantitative Evaluation
4.2.3. Qualitative Evaluation
4.2.4. Model Size and Inference Time
4.3. Ablation Studies
4.3.1. Effectiveness of the PSA Polarized Attention Mechanism
4.3.2. Effectiveness of the CMF Module
4.3.3. Effectiveness of the AFFM Module
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | NJU2K | NLPR | SIP | STERE | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CTMF18 [27] | 0.849 | 0.845 | 0.913 | 0.085 | 0.860 | 0.825 | 0.929 | 0.056 | 0.716 | 0.694 | 0.829 | 0.139 | 0.848 | 0.831 | 0.912 | 0.086 |
PCF18 [28] | 0.877 | 0.872 | 0.924 | 0.059 | 0.874 | 0.841 | 0.925 | 0.044 | 0.842 | 0.838 | 0.901 | 0.071 | 0.875 | 0.860 | 0.925 | 0.064 |
AFNet19 [30] | 0.772 | 0.775 | 0.853 | 0.100 | 0.799 | 0.771 | 0.879 | 0.058 | 0.720 | 0.712 | 0.819 | 0.118 | 0.825 | 0.823 | 0.887 | 0.075 |
CPFP19 [29] | 0.878 | 0.877 | 0.923 | 0.053 | 0.888 | 0.867 | 0.932 | 0.036 | 0.850 | 0.851 | 0.903 | 0.064 | 0.879 | 0.874 | 0.925 | 0.051 |
SSF20 [31] | 0.899 | 0.896 | 0.935 | 0.043 | 0.914 | 0.896 | 0.953 | 0.026 | 0.876 | 0.882 | 0.922 | 0.052 | 0.893 | 0.890 | 0.936 | 0.044 |
CoNet20 [13] | 0.895 | 0.893 | 0.937 | 0.046 | 0.908 | 0.887 | 0.945 | 0.031 | 0.858 | 0.867 | 0.913 | 0.063 | 0.908 | 0.905 | 0.949 | 0.040 |
D3Net21 [8] | 0.900 | 0.900 | 0.950 | 0.041 | 0.912 | 0.897 | 0.953 | 0.030 | 0.860 | 0.861 | 0.909 | 0.063 | 0.899 | 0.891 | 0.938 | 0.046 |
DCMF22 [32] | 0.913 | 0.922 | 0.925 | 0.043 | 0.922 | 0.914 | 0.937 | 0.029 | 0.875 | 0.774 | 0.844 | 0.115 | 0.910 | 0.906 | 0.914 | 0.043 |
DMRA22 [34] | 0.886 | 0.897 | 0.921 | 0.051 | 0.899 | 0.888 | 0.942 | 0.031 | 0.806 | 0.852 | 0.863 | 0.085 | 0.886 | 0.895 | 0.934 | 0.047 |
MMNet22 [33] | 0.910 | 0.919 | 0.922 | 0.038 | 0.925 | 0.918 | 0.955 | 0.023 | 0.824 | 0.860 | 0.871 | 0.080 | 0.901 | 0.910 | 0.941 | 0.040 |
CMINet22 [52] | 0.916 | 0.924 | 0.945 | 0.034 | 0.922 | 0.919 | 0.955 | 0.024 | 0.892 | 0.916 | 0.930 | 0.043 | 0.905 | 0.912 | 0.946 | 0.038 |
DSU22 [53] | 0.900 | 0.909 | 0.926 | 0.036 | 0.919 | 0.907 | 0.957 | 0.022 | 0.870 | 0.895 | 0.918 | 0.050 | 0.886 | 0.895 | 0.927 | 0.037 |
CFIDNet22 [35] | 0.914 | 0.923 | 0.913 | 0.038 | 0.922 | 0.915 | 0.950 | 0.026 | 0.864 | 0.891 | 0.905 | 0.060 | 0.901 | 0.908 | 0.924 | 0.043 |
SPNet23 [9] | 0.924 | 0.934 | 0.953 | 0.028 | 0.927 | 0.925 | 0.959 | 0.021 | 0.893 | 0.916 | 0.929 | 0.043 | 0.906 | 0.914 | 0.943 | 0.037 |
DAL23 [36] | 0.904 | 0.909 | 0.940 | 0.044 | 0.930 | 0.918 | 0.966 | 0.023 | 0.898 | 0.898 | 0.929 | 0.047 | 0.902 | 0.904 | 0.941 | 0.040 |
EGA-Net23 [37] | - | 0.883 | 0.922 | 0.033 | - | 0.912 | 0.966 | 0.020 | - | 0.877 | 0.922 | 0.048 | - | 0.870 | 0.924 | 0.042 |
RD3D24 [38] | 0.916 | 0.914 | 0.947 | 0.036 | 0.930 | 0.920 | 0.965 | 0.022 | 0.885 | 0.889 | 0.924 | 0.048 | 0.911 | 0.906 | 0.947 | 0.037 |
CMANet (Ours) | 0.925 | 0.936 | 0.954 | 0.028 | 0.933 | 0.932 | 0.966 | 0.019 | 0.893 | 0.920 | 0.930 | 0.043 | 0.907 | 0.917 | 0.943 | 0.036 |
Model | NJU2K | NLPR | SIP | STERE | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ours*-Res2Net-101 | 0.924 | 0.933 | 0.951 | 0.029 | 0.930 | 0.929 | 0.961 | 0.020 | 0.896 | 0.917 | 0.932 | 0.041 | 0.910 | 0.918 | 0.944 | 0.035 |
Ours-Res2Net-50 | 0.925 | 0.936 | 0.954 | 0.028 | 0.933 | 0.932 | 0.966 | 0.019 | 0.892 | 0.920 | 0.930 | 0.043 | 0.907 | 0.917 | 0.943 | 0.036 |
Model | Ours | DMRA22 | DSU22 | CFIDNet22 | DAL23 | SPNet23 | EGANet23 |
---|---|---|---|---|---|---|---|
Model Size (MB) | 145 | 187 | 96 | 55 | 6 | 174 | 170 |
Inference Time (ms) | 31 | 50 | 48 | 46 | 38 | 84 | 33 |
Model | NJU2K | NLPR | SIP | STERE | ||||
---|---|---|---|---|---|---|---|---|
Ours | 0.925 | 0.028 | 0.933 | 0.019 | 0.892 | 0.043 | 0.907 | 0.036 |
A | 0.924 | 0.028 | 0.929 | 0.020 | 0.883 | 0.049 | 0.902 | 0.040 |
B1 | 0.924 | 0.030 | 0.933 | 0.020 | 0.890 | 0.043 | 0.906 | 0.036 |
B2 | 0.924 | 0.029 | 0.930 | 0.022 | 0.892 | 0.044 | 0.905 | 0.039 |
B3 | 0.920 | 0.029 | 0.926 | 0.021 | 0.889 | 0.045 | 0.902 | 0.040 |
C1 | 0.925 | 0.029 | 0.931 | 0.020 | 0.896 | 0.042 | 0.908 | 0.036 |
C2 | 0.923 | 0.030 | 0.931 | 0.020 | 0.891 | 0.045 | 0.908 | 0.037 |
C3 | 0.922 | 0.030 | 0.930 | 0.021 | 0.953 | 0.030 | 0.906 | 0.037 |
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Du, Q.; Bian, Y.; Wu, J.; Zhang, S.; Zhao, J. Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection. Appl. Sci. 2024, 14, 7440. https://doi.org/10.3390/app14177440
Du Q, Bian Y, Wu J, Zhang S, Zhao J. Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection. Applied Sciences. 2024; 14(17):7440. https://doi.org/10.3390/app14177440
Chicago/Turabian StyleDu, Qinsheng, Yingxu Bian, Jianyu Wu, Shiyan Zhang, and Jian Zhao. 2024. "Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection" Applied Sciences 14, no. 17: 7440. https://doi.org/10.3390/app14177440