# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Segmentation  
Image segmentation tasks such as semantic segmentation, instance segmentation, or panoptic segmentation solely predict the visible shape of the objects in a scene. Therefore, these tasks mainly operate with modal perception. Amodal segmentation, on the other hand, works with amodal perception. It estimates the shape of an object beyond the visible region, i.e., the visible mask (also called the modal mask) and the mask for the occluded region, from the local and the global visible visual cues (see Figure 3). Amodal segmentation is rather challenging, especially if the occluder is of a different category (e.g., the occlusion between vehicles and pedestrians). The visible region may not hold sufficient information to help in determining the whole extent of the object. Contrariwise, if the occluder is an instance of the same category (e.g., occlusion between pedestrians), since the features of both objects are similar, it becomes difficult for the model to estimate where the boundary of one object ends and the second one begins. In either case, the visible region plays a significant role in guiding the amodal mask generation process. Therefore, most existing methods require the modal mask as input. To  
Figure 2: The three sub-tasks in amodal completion. alleviate the need for a manually annotated modal mask, many works apply a pre-trained instance segmentation network to obtain the visible mask and utilize it as input. In the following, we describe the architecture of the GAN-based models that are used in generating the amodal mask of the occluded objects. **A two hourglass generator:** [PERSON] et al. [60] apply a pre-trained instance segmentation network on the input image to obtain an initial mask and feeds it to a two-stage pipeline for human deocclusion. Given the initial mask, the generator implements two hourglass modules to refine and complete the modal mask to produce the amodal mask at the end. A discriminator enhances the quality of the output amodal mask. An additional parsing result accompanies the result of the generator, which is employed by a Parsing Guided Attention (PGA) module to reinforce the semantic features of body parts at multiple scales as a part of a parsing guided content recovery network. The latter uses a combination of UNet [61] and partial convolutions [62] in generating the content of the invisible area. The additional parsing branches add extra semantic guidance, which improves the final invisible mask. **A coarse-to-fine architecture with contextual attention:** [PERSON] et al. [63] firstly employ a contour detection module to extract the visible contour of an object and then complete it through a contour completion network. The contour detection module uses DeepCut [64] to segment prominence objects, and performs noise removal and edge detection to extract the incomplete contour of the object from the segmentation map. Then, the contour completion network learns to conjecture the foreground contour. The contour completion network is composed of a generator and a discriminator. The generator has a coarse-to-fine architecture, each with a similar encoder-decoder structure, except that the refinement network employs a contextual attention layer [65]. Finally, the completed contour along with the ground-truth image are fed to the discriminator which produces a score map to indicate the originality of each region in the generated contour mask and can decide whether the mask aligns with the contour of the image. The discriminator is a fully convolutional PatchGAN [8] trained with a hinge loss. The results show that the contour completion step assists in the explicit modeling of the background and the foreground layer borders, which leads to less evident artifacts in the completed foreground objects. **A generator with priori knowledge:** The authors of [66] also utilize a pre-trained instance segmentation model to obtain the visible human mask, which is fed with the input image into a GAN-based model to produce the amodal mask of occluded humans.