# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 3 Task 1. Building the Foundation for Organizing and Curating the Raw Image Data
### 3.2. Curation: Adding Value
#### 3.2.1 Extraction
**Fast R-CNN**[47]: Like R-CNN, SPP-Net has multiple stages of extracting the feature vector, feeding to SVM for classification, and refining bounding box regions. Fast R-CNN solved this issue by altering the architecture of the CNN in SPP-Net so that the feature vector layer is then connected to two sibling output layers. One is a softmax layer for classifying objects, and one is regarding refining the position of the object's bounding box. **Faster R-CNN**[49]: The problem with the proposed approaches is selecting the suitable regions in a costly way. In Faster R-CNN, a Region Proposal Network (RPN) is proposed that could propose accurate bounding boxes for the objects in an image cost-effectively. Adding RPN to the CNN architecture leads to a more efficient and faster solution. **ORE**[60]: The Open-World Object Detector (ORE) is a new method for detecting objects in the open world using contrastive clustering and energy-based identification. * **Single-Stage. ** Single-stage models are end-to-end frameworks that perform the region selection and object detection with the same model; hence, the computation cost is drastically reduced in these models. Here, we review some of the popular models in this category. * **YOLO v1. **[50]: In this method, the image is divided into SxS grids, and for each cell of the grid, the bounding boxes plus their confidence scores and the object that is centered on that cell plus its confidence score are calculated. Finally, the prediction of an object will be a function of the two confidence scores of the bounding box and object. * **SSD**[51]: The problem with YOLO is that sometimes it might not detect small objects very well due to its fixed-size grids. SSD solves this issue by proposing a more accurate and faster solution using a set of anchor boxes with different scales to make a discrete output space. * **RetinaNet**[52]: RetinaNet uses a new loss function (also known as Focal Loss) which forces the model to focus on misclassified samples in the training stage. By doing so, the model could reach a comparable accuracy to the two-stage models. * **NAS-FPN**[56]: By utilizing a feature pyramid network, this method intends to learn a more reasonable object detection architecture without designing it manually. This architecture aggregates features at multiple scales. * **EfficientDet**[58]: This method examines the design options for neural network models for object detection and suggests critical optimizations to enhance efficiency. These optimizations are used to design a novel family of detectors called EfficientDet. Feature networks and class prediction networks need to be scaled up to optimize accuracy and efficiency, as the authors claim. **Extracting Additional Information. ** The same object detection approaches and other deep learning models could also be used to detect the image's relations, concepts, and keywords. In some studies, the detected objects are referred to as concepts [62; 64]. In another study, topic extraction was addressed from the image [53]. The image is first fed to a pre-trained neural network to extract the features, those features are used as the input to a topic generation model, and the related topics are extracted. RW-AIA [59] finds image keywords by considering label dependencies and modeling the relationship between these labels. They construct a vocabulary graph and, by applying graph convolutional networks (GCNs), find the tag descriptors. Finally, QAIA [61] uses a reweighting quantization function to calculate the similarity between each pair of image keywords in the vocabulary graph.