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1. What is typically meant by 3D-face recognition? We are usually extracting the face encoding from 2D-images, right? Yes. The goal is to reconstruct the three-dimensional shape, as well as the texture of a face from a single or multiple images of that person. In recent years, "the performance of 2D face recognition algorithms has significantly ...


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If I understood well you have 2 questions. How to get the bounding box given the network output What Smooth L1 loss is The answer to your first question lies in the equation (2) in the section 3.2.1 from the Faster R-CNN paper. As all anchor based object detector (Faster RCNN, YOLOv3, EfficientNets, FPN...) the regression output from the network are not ...


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As you ask, "in general...", I will answer generally, however this changes a lot from model to model and the way they handle close objects. In general, yes, they would do a poor job detecting very close objects, switch to segmentation models for that (for class or better, instance segmentation). In general, objects detectors learn to tell an object ...


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Yes, it is not specified because the region proposal algorithm did not change from R-CNN (the previous version from Fast R-CNN, however, in the next verion, Faster R-CNN, this algorithm is replaced by a CNN). The region proposal algorithm you are looking for is called selective search. You can find in the R-CNN paper that the algorithm is described in "...


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Check this page out, it describes how to develop apply triplet loss to a network: https://towardsdatascience.com/image-similarity-using-triplet-loss-3744c0f67973


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One could imagine using a segmentation network as a first step of processing. Then feeding an area corresponding to a bounding box of each segmented object to the classifier. Potentially that could yield an increase in performance in classifying objects in an image, but not without a cost of training time, sine suddenly there are two networks to train ...


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Assuming all of the tables will be oriented in similar ways (label and value running horizontally) and that all writing will be printed rather than handwritten, one solution method would be to use an image segmentation method such as edge detection to segregate these horizontal (label, value) pairs and then use a library like Tesseract for OCR. There are ...


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