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To understand homographies and how to find them, you will need a good dose of projective geometry. I will briefly describe some preliminary concepts that you need to know before trying to find the homography, but don't expect to understand all these concepts with one reading iteration and only by reading this answer, if you are not familiar with them, ...


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In fact, autoencoders are used for generative tasks. Have a look at Tutorial on Variational Autoencoders (VAEs). The coolest thing about VAE is that abstract features can be easily amplified or suppressed based on extracted vectors from the latent space. Let's imagine a model trained on MNIST to generate digits. If you take two images of the same digit which ...


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Auto-encoders are widely used and maybe even more used than GANs (in fact, auto-encoders are older than GANs, although the main general idea behind GANs is quite old). For example, auto-encoders are used in World Models, for drug design (e.g. see this paper) and many other tasks that involve data compression or generation. So, if we train autoencoders, for ...


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This is just an idea Given a set of pixels, the task is to decide: Which pixel is the center of an object? What is the size of the bounding boxes with the center is the pixel in part 1? Formula, consider this is a 2D image, call $(x,y)$ is the horizontal and vertical coordinate and $(w_i,h_i)$ is the size of bouding box of object $i$: $\text{For }m \in[x,x+...


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The main distinction between tasks is 'classification' vs 'regression'. In classification you would try to identify the presence of a cloud or not in an image, if you want to predict the level of 'cloudness' with continuous values you are then performing a regression task. I'm not aware about state-of-the models specific for images, but you can potentially ...


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One of the methods which is quite fast and easy to implement. You can do Principal Component Analysis (PCA) based face recognition. You can go through this paper for the theory behind it. For an example implementation you can see this blog post. The process, roughly, is as following: If you have a grayscale image of size $(20,20)$, then this image can be ...


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Yes it should be possible. You may have a bug in your code, or the wrong hyperparameters. Training ResNet-50 will take a long time. Try training on other sets of images and see what accuracy you get to check if your approach is correct. Or, try loading a pretrained model, and training from that.


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There are tasks in computer vision where recurrent neural networks (RNNs) can be useful because there's some sequential sub-task in the main task. For instance, in the paper Long-Term Recurrent Convolutional Networks for Visual Recognition and Description, the authors investigate the use of a neural network that is both recurrent and convolutional to solve ...


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After a quick scan, it would seem that, in the history of object detection, machine learning has always been at the forefront. Before then, it would just be a heuristic approach. For a quick answer, here: https://towardsdatascience.com/real-time-object-detection-without-machine-learning-5139b399ee7d That goes over object detection without using machine ...


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You should use both classes together. Let's say you use the method you proposed. Then they will be contradicting each other as one teaches the network to recognize people, not objects and the other teaches the network to recognizes objects not person. There is no need for seperation of the two classes, unless you are making two seperate classifier. Hope I ...


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There are hundred of papers on this task some older than I am! Normally this is done by trying to form a box shape around the image than estimate the volume. This task is typically done with multiple images so the two can generate a more clear picture of the size of the object than one image alone. An object could be 'infinitely' large but its mass could be ...


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Bot development is more about 'hacking' than AI in a way that in the very first place you need to read and (over) write game data which you are not supposed to (and thereby potentially violating the Terms and Conditions - so be aware of that). The AI part is fairly simple for most hack/bot applications that I can think of. Read data To read game data you ...


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If the measurements you want from the object aren't too complicated (ie. length of a clearly defined feature), and if you are able to acquire a training dataset of images of the objects similar to what your model will see in your use case (same scale/distance), their bounding boxes and their measurements, a model you could try to implement is a Multi-Task ...


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Father Ted explains why this is a hard problem. Seriously -- if you have stereo images it should be possible, since that's what we use for depth perception. If you know how far away points x1 and x2 are, then you can measure distance using trigonometry. No neural networks needed, I guess. https://en.wikipedia.org/wiki/Triangulation_(computer_vision)


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The abbreviations sim2sim, sim2real and real2real refer to techniques that can be used to transfer knowledge from one environment (e.g. in simulation) to another one (e.g. in the real world). sim2sim stands for simulation-to-simulation, sim2real stands for simulation-to-real, and real2real stands for real-to-real. In sim2sim, knowledge acquired during ...


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The paper you are citing is the paper that introduced the cascaded convolution neural network. In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the introduction of a new input feature which ...


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Since you have already tried U-Net. You may look into Siamese Networks (with CNNs for images), they are very well known for computing similarity via deep learning. This is a central idea and can be performed with both text and images. As a tip, you may be able to leverage a lot of architecture from U-Net to Siamese. Hope it helps, Some useful links to ...


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I think you underestimate the size of YOLO. This is the size of one segment of yolo tiny according to the darknet .cfg file: Convolutional Neural Network structure: 416x416x3 Input image 416x416x16 Convolutional layer: 3x3x16, stride = 1, padding = 1 208x208x16 Max pooling layer: 2x2, stride = 2 208x208x32 ...


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Assuming you pass through the entire validation dataset, this can't be due to shuffling since you still compute the loss/accuracy over the entire dataset, so order does not really matter here. It is more likely that you have a significantly smaller or less representative validation dataset, e.g., distribution of the validation dataset can be skewed towards ...


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There is no easy rule for this. You can use transfer learning to select a model that works well on image classification. However the accuracy you achieve will be highly dependent on your training set. If your training set is "similar" in quantity and quality to what was used for the accuracy achieved by the transfer learning model in some application you ...


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ResNet is an architecture for object recognition and you may use it to do your classification task. Fast RCNN may improve your results but is a more difficult architecture to implement. If you want to go in this direction the best place to start is the arxiv paper of the Fast R-CNN (arxiv.org/abs/1504.08083). If I am not wrong, there is an implementation ...


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Image to Image translation is the task of transferring an image's characteristics from one domain and representing it in another. GANs have provided an end to end method to do this task. Prior to Gans, these tasks were done individually, by using classic image processing techniques mainly. Techniques such as image denoising, or finding edges in photos, or ...


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The paper A Brief Introduction to Statistical Shape Analysis (2002) by M. B. Stegmann and D. D. Gomez provides a definition of a landmark in the context of statistical shape analysis, which I will report below. Definition 1: Shape is all the geometrical information that remains when location, scale and rotational effects are filtered out from an object. ...


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The short answer is: yes, it could. In what you are describing, there's nothing very new or specific conceptually; it sounds like a standard regression task. Now the problem that you're actually facing is: do you have the data? Algorithms won't be able to learn the distance between eyes if you don't have the data that it takes. It could be supervised labels ...


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Introduction Bag-of-features (BoF) (also known as bag-of-visual-words) is a method to represent the features of images (i.e. a feature extraction/generation/representation algorithm). BoF is inspired by the bag-of-words model often used in the context of NLP, hence the name. In the context of computer vision, BoF can be used for different purposes, such as ...


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(Of course, similar questions have been asked in the past and there are many sites, papers, video lessons, online that explain how CNNs work, but I think it's still a good idea to have a reference answer that hopefully will give you the main ideas behind CNNs.) A convolutional neural network (CNN) is a neural network that performs the convolution (or cross-...


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There are many computer vision (CV) algorithms and models that are used for different purposes. So, of course, I cannot list all of them, but I can enumerate some of them based on my experience and knowledge. Of course, this answer will only give you a flavor of the type of algorithm or model that you will find while solving CV tasks. For example, there are ...


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If the model is trained and held constant, then there are so-called adversarial attacks to modify images such that the model classifies them incorrectly (see Attacking Machine Learning with Adversarial Examples). However, if you want to make images that are untrainable, you are probably out of luck. Deep neural networks can learn to recognize even random ...


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You should not use augmented data in the validation nor in the test set. Validation and test set are purely used for hyperparameter tuning and estimating the final performance, i.e. estimating the generalization error. These two data sets should be as close as possible to other data, which you could have acquired, but you actually haven not, i.e. your true ...


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try using an adjustable learning rate. Keras has a number of callbacks that are useful for this purpose. The ReduceLROnPlateau callback can be used to monitor validation loss and reduce the learning rate by a factor if the validation loss does not decrease after a user specified number of epochs. The ModelCheckpoint callback is useful to monitor the ...


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