# Self-Improved Learning for Salient Object Detection
## 2 Related Work  
### Network Designs for SOD  
The network designs for Salient Object Detection can be summarized into three categories: architecture-based, aggregation-based, and contour-based methods.  
First, architecture-based methods explore various feature extractors for SOD. Early methods such as [11; 12; 13; 14; 15; 16; 17] obtain handcrafted or deep features from image subunits and use MLP classifiers to predict saliency scores based on the features. For example, Super-CNN [16] and Multi-Context [13] use super-pixels or patches as image subunits. These methods fail to model the global context and cannot be trained end to end. Inspired by FCNs [18], CNN-based methods [19; 20; 21; 22; 23; 24; 25] formulate the salient object detection as a pixel  
Figure 1: **(a)** The four giraffes are labeled as equally salient regardless of their different sizes and location in the image. **(b)** The bike on the left is from PASCAL-S [8] and the right is from DUT-OMRON [9]. The butterfly on the left is from ECSSD [10] and the right is from HKU-IS [11].  
level prediction task and generate the saliency map in an end-to-end manner. Recently, Transformer has also been applied to SOD [26; 27; 28; 29; 30]. They combine both Transformer and CNN to process multi-level features. For instance, VST [30] considers the SOD task from the sequence-to-sequence perspective and designs a pure transformer model to handle the tokenized input. In addition, Transformer is also utilized to model the cross-modal information from the RGB and depth data [26; 27].  
Second, aggregation-based methods focus on feature aggregation or feature fusion to combine features from different layers. These methods capture both spatial details and global semantics for an accurate saliency map [13; 24; 31; 32; 33; 34; 35; 36]. A simple yet effective way is to skip connect the features from the encoder to the features with the same spatial size from the decoder. This U-shape-based structure has been employed in many works [13; 35; 36]. Furthermore, GateNet [34] introduces gated mechanisms to control the information fusion flow. Also, channel-wise attention and spatial-wise attention have been applied to weigh the importance during the feature aggregation process [31; 32; 33]. MENet [37] designs a multi-scale feature enhancement module to gradually aggregate and refine global or detailed features by changing the size order of the input feature sequence.  
To achieve high-quality saliency prediction with fine edge details, contour-based methods attempt to explicitly regularize the contours of salient objects. This line of work involves two aspects: network design and loss function design. Contour-aware networks attempt to model contours explicitly [32; 33; 38; 39]. Specifically, EGNet [38] proposes to make the model predict saliency and contours simultaneously. Based on this, C2 SNet [39] adopts an alternating converting method between contour maps and saliency maps. Unlike existing two-branch methods [33; 38] that use one branch for the boundary and the other one for the entire object, MENet [37] uses two parallel branches that learn internal regional features and global features, respectively, so as to reduce the interference of inaccurate boundary information with global features. Some other works design contour-aware loss functions. The structural similarity index (SSIM) [40] and weighted BCE loss [41] are introduced for more edge details in the saliency maps.  
Our method aims to smoothen the learning process and improve the robustness of SOD. We do not design a specific architecture or model contours for SOD explicitly. Instead, our learning strategy is capable of working with all these methods without modifying their networks.