# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 2 Related Work
### Deep-Learning-Based Techniques  
In deep learning, a model learns directly from image to perform classification. [PERSON] et al. [11] developed a technique that makes use of prior saliency-detection techniques. Repetitive architecture allows for this method to refine the saliency map by rectifying previous errors, which results in fine predictions. They introduced a pretraining strategy using segmentation data for network training, which helps in segmentation or successful training and allows for the network to grasp objects for saliency detection. They trained an FCN to estimate the nonlinear mapping of saliency values from raw pixels and neglect saliency priors; then, these saliency priors were combined with deeply learned features, which are passed for iterative refinement. In this module the whole network is propagated forward, which causes an increase in computational cost and memory usage. [PERSON] et al. [12] used many saliency-detection methods to improve the quality of saliency map. First, they generated initial saliency maps by selecting saliency models. Then, they integrated these maps to form a binary map after that final saliency map had been generated using integration logic. The combined binary map defines pixels in finer way than a single binary map does. By using these labels, the final saliency map is produced by using logic in which maximal and minimal saliency values are assigned to pixels. The efficiency of this method relies on the saliency-detection model being selected. Therefore, the method of selecting existing techniques plays a key role. This method performs well when selected methods can detect images, and fails when those methods cannot. [PERSON] et al. [13] introduced a model to enhance segmentation results near boundaries. An attentive feedback module was proposed to produce fine boundaries. Features from encoder blocks are passed to decoders where the feedback module is applied for segmentation. This module learns the structure and performs segmentation. Further, they proposed boundary-enhancement loss to assist the feedback module. VGG 16 was used as a base model by modifying it into an encoder network, followed by perception module that uses a fully connected layer that leads to the decoder module. Every decoder layer had a 3 \(\times\) 3 convolutional layer. Multiscale features are collected via the encoder block; then, global saliency prediction is calculated, which is passed to the decoder for finer saliency predictions. For boundary correction, attentive feedback is applied. [PERSON] et al. [14] proposed a deep model that uses boundary knowledge to accurately locate salient objects. In boundary-guided networks, two subnetworks are defined: one for the mask and the other for boundaries; features are shared between these two networks. They also proposed focal loss to learn the loss of hard pixels near boundaries. Both subnetworks follow encoder decoder networks, features are extracted in the encoder, and the decoder gives the output. Decoders of both subnetworks are connected. The features extracted by the encoder have different resolutions, and bidirectional flow of information is enabled in encoders. Feature maps from two subnetworks are combined and passed to a convolutional layer. The decoders of mask and boundary subnetworks are connected. The mask decoder refines the mask prediction and uses focal loss, which focuses on boundary pixels. [PERSON] et al. [15] developed a robust and versatile detection technique that enhances the mean average precision by over 50% compared to prior techniques. This approach incorporates two concepts: (i) applying high CNNs to localise objects from bottom-up regions; and (ii) supervision pertaining to when abundant data are available, followed by fine tuning to increase performance. The whole technique comprises three modules. First, the module originates the regions. The second module consists of convolutional layers to collect features from regions. The final module is linear SVMs.