## 2 Related Work  
In the literature, image processing, computer vision, and pattern recognition-related research studies have received a lot of attention in recent years due to advancements in algorithms and equipment used in their application, which varies from object detection [3] and object tracking [4], to 3D modeling [5]. The Cascade Classifier, the first method we analyzed for object detection, has shown significant success for object detection tasks in the past. [PERSON] and [PERSON] [6] present a comparison of several different cascade of classifier detectors with high detection rates for face detection, with low false-positive rates, which is typically a significant issue with cascade of classifier models. A similar study performed by [PERSON] et al. [7] also presents successful results for different classifier boosting strategies applied to cascade of classifier type models trained for face detection. [PERSON] et al. [8] expand the usage of Cascade Classifiers to the general case with the PASCAL VOC datasets (20 object classes) and the ImageNet dataset (200 object classes). CNNs have become widely popular for carrying out object detection tasks in recent years. A notable study presented by [PERSON] et al. [9] for roof damage detection uses their own CNN model to perform the detection with excellent accuracy. A more general building damage study by [PERSON] et al. [10] utilizes CNNs with a morphological filter method for damage candidate region proposals. [PERSON] et al. [11] compare a number of CNN architectures for post-hurricane damage detection with a high mean average precision. Some recent methods have combined the cascading strategy with CNNs, as presented by [PERSON] and [PERSON] [12]. In their paper, the researchers use a cascading region with a sequentially higher intersection over union thresholds to filter out false-positive samples. Looking into the Visual Question Answering (VQA) approach, there has been considerable research conducted on this new model recently. Studies [13; 14; 15; 16; 17; 18; 19; 20; 21] have made significant efforts in order to develop and analyze the approach. These studies propose different approaches for the union of semantic images and question features. VQA is often used for extracting information from remotely sensed data [22]. [PERSON] et al. introduced a model based on deep learning to analyze images and corresponding questions. They used both recurrent and convolutional neural networks in their VQA model, and they had promising results to answer specific questions regarding buildings in the images [22]. [PERSON] et al., on the other hand, presented a human recognition application in VQA [23]. In their study, they introduced a novel feature extractor in order to estimate 2D poses of humans, which then leads to an estimation of action by humans. Their results showed a 62.03% mean average precision for a very large video dataset used in their study [23]. In a similar study, [PERSON] et al. introduced VQASTO, which is a task ontology-based Visual Question Answering method for action surveillance [24]. In that study, two subtasks are used: skeleton-based action recognition and pose estimation/tracking. With these subtasks, the system maps question sentences to the corresponding action and outputs an answer [24]. One significant application area of VQA is the medical field [25]. The idea for models in this field is to answer medical questions based on the visual content of images, for instance radiology images [25]. [PERSON] et al. presented the results of 17 teams that tried to achieve the best VQA results, and the team with best result had an accuracy of 62.4% on 4200 radiology images. VQA also is used for specific application purposes in the literature, such as the detection of occupancy spaces for parking. [PERSON] et al. introduced a VQA model, which is built by manually labeling questions and answers on the CNRPark + Ext dataset [26]. In their study, they ended up with an average accuracy of 94.31% for detecting parking spots [26].