# Advancing in RGB-D Salient Object Detection: A Survey
## 3 Attention Mechanisms in RGB-D SOD
### Classification of Attention Mechanisms  
In the field of RGB-D SOD, attention mechanisms have evolved from basic spatial attention to more complex forms, such as channel attention and multidimensional attention, refining the processes of feature selection and cross-modal integration. The application of attention in RGB-D SOD is particularly useful for addressing the challenges posed by differences in modal quality, such as, for example, by weighting the features derived from each modality when the depth data are noisy or incomplete, the attention mechanism mitigates the effects of low-quality data and enhances the model's ability to accurately detect salient objects [35; 36]. **Self-attention**[24; 37]. Processing a single data stream by, for example, solely RGB or depth data, refines feature representations by evaluating the relevance of input features within their context. When dealing with noisy depth data, self-attention effectively captures global dependencies within the input, identifies and highlights critical features in complex geometric structures, and suppresses noise interference. **Mutual or co-attention**[38]. The processing of multiple modalities aligns and synchronizes feature representations. This is particularly crucial in RGB-D data, where the correspondence between RGB and depth features directly impacts detection performance. When addressing the challenges posed by noisy depth data, mutual attention ensures an accurate alignment between RGB and depth features, thereby reducing the error propagation caused by noise. **Channel attention**[33]. Channel attention improves model detection performance by assigning different weights to each feature channel, enhancing important features while suppressing less relevant ones. In RGB-D SOD tasks, channel attention effectively distinguishes critical features from redundant information in both RGB and depth data. When noise is present in depth data, the channel attention mechanism automatically reduces the weights of noisy channels, highlighting more reliable ones. By optimizing the representation of feature channels, this mechanism enables the model to more accurately process varying depth information while maintaining high sensitivity to key visual details, thereby enhancing overall detection performance. **Multi-head attention**[39]. Multi-head attention captures multiple representations of input data by computing several self-attention mechanisms in parallel, allowing it to focus on different aspects of the input from various perspectives, leading to a more comprehensive feature representation. In RGB-D SOD tasks, multi-head attention effectively handles noisy depth data and enhances spatial resolution. By processing multiple attention heads simultaneously, it identifies and emphasizes meaningful information in different feature spaces while suppressing irrelevant features associated with noise. This enables the model to capture and focus on key features in RGB and depth data from different perspectives, enhancing feature representation across scales and improving both spatial resolution and target detection accuracy. **Spatial attention**[40]. Spatial attention assigns different weights to features at various locations within an image, emphasizing important regions. In RGB-D SOD tasks, spatial attention effectively identifies and highlights key areas within the image. When dealing with noisy depth data, spatial attention assists the model in ignoring regions heavily affected by noise, allowing it to focus on parts that contain significant spatial information. **Hybrid attention**[25; 41; 42]. Hybrid attention is a comprehensive strategy that combines multiple attention mechanisms, leveraging the strengths of channel attention, spatial attention, and self-attention to capture salient features across different levels and dimensions. In RGB-D SOD tasks, hybrid attention effectively addresses challenges related to noisy depth data and spatial resolution.