Questions tagged [computer-vision]
For questions related to computer vision, which is an interdisciplinary scientific field (which can e.g. use image processing techniques) that deals with how computers can be made to gain high-level understanding from digital images or videos. For example, image recognition (that is, the identification of the type of objects in an image) is a computer vision problem.
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How is it possible that deep neural networks are so easily fooled?
The following page/study demonstrates that the deep neural networks are easily fooled by giving high confidence predictions for unrecognisable images, e.g.
How this is possible? Can you please ...
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What is a fully convolution network?
I was surveying some literature related to Fully Convolutional Networks and came across the following phrase,
A fully convolutional network is achieved by replacing the parameter-rich fully ...
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Are information processing rules from Gestalt psychology still used in computer vision today?
Decades ago there were and are books in machine vision, which by implementing various information processing rules from gestalt psychology, got impressive results with little code or special hardware ...
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Is it possible to train a neural network to estimate a vehicle's length?
I have a large dataset (over 100k samples) of vehicles with the ground truth of their lengths.
Is it possible to train a deep network to measure/estimate vehicle length?
I haven't seen any papers ...
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Is it difficult to learn the rotated bounding box for a (rotated) object?
I have checked out many methods and papers, like YOLO, SSD, etc., with good results in detecting a rectangular box around an object, However, I could not find any paper that shows a method that learns ...
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Do deep learning algorithms represent ensemble-based methods?
According to the Wikipedia article on deep learning:
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep ...
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Extending FaceNet’s triplet loss to object recognition
FaceNet uses a novel loss metric (triplet loss) to train a model to output embeddings (128-D from the paper), such that any two faces of the same identity will have a small Euclidean distance, and ...
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In Computer Vision, what is the difference between a transformer and attention?
Having been studying computer vision for a while, I still cannot understand what the difference between a transformer and attention is?
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Why does nobody use decision trees for visual question answering?
I'm starting a project that will involve computer vision, visual question answering, and explainability. I am currently choosing what type of algorithm to use for my classifier - a neural network or a ...
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In YOLO, what exactly do the values associated with each anchor box represent?
I'm going through Andrew NG's course, which talks about YOLO, but he doesn't go into the implementation details of anchor boxes.
After having looked through the code, each anchor box is represented by ...
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Is it okay to use publicly available Instagram videos to train an AI?
Since I haven't found any good training data for my university project, I want to use pictures and videos from public Instagram profiles. Am I allowed to do that?
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What are the main algorithms used in computer vision?
Nowadays, CV has really achieved great performance in many different areas. However, it is not clear what a CV algorithm is.
What are some examples of CV algorithms that are commonly used nowadays and ...
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What are sim2sim, sim2real and real2real?
Recently, I always hear about the terms sim2sim, sim2real and real2real. Will anyone explain the meaning/motivation of these terms (in DL/RL research community)?
What are the challenges in this ...
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What are the state-of-the-art approaches for detecting the most important "visual attention" area of an image?
I'm trying to detect the visual attention area in a given image and crop the image into that area. For instance, given an image of any size and a rectangle of say $L \times W$ dimension as an input, I ...
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What could an oscillating training loss curve represent?
I tried to create a simple model that receives an $80 \times 130$ pixel image. I only had 35 images and 10 test images. I trained this model for a binary classification task. The architecture of the ...
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Term for algorithms that are not trained
Before the advent of neural architectures, many AI domains (e.g. speech recognition and computer vision) used algorithms that consisted of a series of hand-crafted transformations for feature ...
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What algorithms are used for image segmentation of images where objects are not composed of pixels that are similar in value?
In the process of segmentation, pixels are assigned to regions based on features that distinguish them from the rest of the image. Value Similarity and Spatial Proximity, for example, are two ...
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Why don't we use auto-encoders instead of GANs?
I have watched Stanford's lectures about artificial intelligence, I currently have one question: why don't we use autoencoders instead of GANs?
Basically, what GAN does is it receives a random vector ...
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Can one use an Artificial Neural Network to determine the size of an object in a photograph?
My question relates to but doesn't duplicate a question that has been asked here.
I've Googled a lot for an answer to the question: Can you find the dimensions of an object in a photo if you don't ...
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Has anybody tried unsupervised deep learning from youtube videos?
YouTube has a huge amount of videos, many of which also containing various spoken languages. This would presumably provide something like the data that a "challenged" baby would experience - "...
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Formal definition of the Object Detection problem
For many problems in computer science, there is a formal, mathematical problem defition.
Something like: Given ..., the problem is to ...
How can the Object Detection problem (i.e. detecting objects ...
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How does the region proposal method work in Fast R-CNN?
I read so many articles and the Fast R-CNN paper, but I'm still confused about how the region proposal method works in Fast R-CNN.
As you can see in the image below, they say they used a proposal ...
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How to use computer vision to find corners of a soccer field based on location coordinates?
I want to use computer vision to allow my robot to detect the corners of a soccer field based on its current position. Matlab has a detectHarrisFeatures feature, but I believe it is only for 2D ...
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Can reinforcement learning algorithms be applied to computer vision problems?
Can reinforcement learning algorithms be applied to computer vision problems? If yes, what are some examples of these applications?
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Is it feasible to train a Machine Learning Model (with image inputs) in an average personal computer?
There are lots of examples of machine learning systems that can recognize objects and extract other information from images with very high precision. To train the models of such systems is necessary (...
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Video summarization similar to Summe's TextRank [closed]
We have the popular TextRank API which given a text, ranks keywords and can apply summarization given a predefined text length.
I am wondering if there is a similar tool for video summarization. ...
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When training a CNN, what are the hyperparameters to tune first?
I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read ...
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How to deal with images of different sizes, which need to be passed to a model of fixed input size, without losing details and spatial information?
I have the following problem while using convolutional neural networks to detect forgeries:
Resizing the image to fit the required input size may not be a good way because the forgery detection ...
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Which neural networks are suitable for visual place recognition?
I am doing a project on visual place recognition in changing environments. The CNN used here is mostly AlexNet, and a feature vector is constructed from layer 3.
Does anyone know of similar work ...
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How can I estimate how many photos I need to train ResNet-50 for image classification?
I am working on a project where I have to classify around 1000 unique objects. I'm trying to plan how much training data I will need to collect. I was planning on using ResNet-50. Is there anyway I ...
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Is the word "pose" used correctly in the paper "Matrix Capsules with EM Routing"?
In traditional computer vision and computer graphics, the pose matrix is a $4 \times 4$ matrix of the form
$$
\begin{bmatrix}
r_{11} & r_{12} & r_{12} & t_{1} \\
r_{21} & r_{...
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Use ConvNet to predict bitmap
I want to build a classifier which takes an aerial image and outputs a bitmap. The bitmap is supposed to be 1 at every pixel where the aerial image has water. For this process I want to use a ConvNet ...
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Which deep learning models are suitable for image-to-image mapping?
I am working on a problem in which I need to train a neural network to map one or more input images to one or more output images (1 channel for image). Below I report some examples of input&output....
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Name of paper for encoding/representing XY coordinates in deep learning
It this podcast between Oriol Vinyals and Lex Friedman: https://youtu.be/Kedt2or9xlo?t=1769, at 29:29, Oriol Vinyals refers to a paper:
If you look at research in computer vision where it makes a ...
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Precise localization and characterization of rudimentary shapes with neural networks
I understand that there are flavors of (convolutional) neural networks that are useful for object localization and detection tasks of reasonable difficulty. In all of the examples I have seen so far, ...
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Why class embedding token is added to the Visual Transformer?
In the famous work on the Visual Transformers, the image is split into patches of a certain size (say 16x16), and these patches are treated as tokens in the NLP tasks. In order to perform ...
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How to calculate the distance between the camera and an object using Computer Vision?
I want to create a Deep Learning model that measures the distance between the camera and certain objects in an image. Is it possible? Please, let me know some resources related to this task.
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Algorithms for scene rotation [closed]
My goal is to take an image and return another image that looks as if the scene was viewed from another angle. The difference in angle can be small — let's say as if the hand holding the camera moved ...
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Why is no activation function used at the final layer of super-resolution models?
I'm trying to implement some image super-resolution models on medical images. After reading a set of papers, I found that none of the existing models use any activation function for the last layer.
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Why would neural network dream scenes mirror the hallucinations people experience when they're tripping?
In DeepDream wikipedia page it's suggested that a dreamlike images created by a convolutional neural network may be related to how visual cortex works in humans when they're tripping.
The imagery ...
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What do the words "coarse" and "fine" mean in the context of computer vision?
I was reading the well know paper Fully Convolutional Networks for Semantic Segmentation, and, throughout the whole paper, they talk use the term fine and coarse. I was wondering what they mean. The ...
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Do Vision Transformers handle arbitrary sequence lengths the same way as normal Transformers?
Does ViT do handle arbitrary sequence lengths using masking the same way the normal Transformer does?
The ViT paper doesn't mention anything about it, so I assume it uses masking like the normal ...
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What are bag-of-features in computer vision?
In computer vision, what are bag-of-features (also known as bag-of-visual-words)? How do they work? What can they be used for? How are they related to the bag-of-words model in NLP?
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Is it true that untrained CNNs can be used as feature extractors?
I've heard somewhere that due to their nature of capturing spatial relations, even untrained CNNs can be used as feature extractors? Is this true? Does anyone have any sources regarding this I can ...
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Could machine learning be used to measure the distance between two objects from a picture or live camera?
Could machine learning be used to measure the distance between two objects from a picture or live camera?
An example of this is the measurement between the centre of each eye pupil.
This area is ...
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Which neural network to use for optical mark recognition?
I've created a neural net using the ConvNetSharp library which has 3 fully connected hidden layers. The first having 35 neurons and the other two having 25 neurons each, each layer with a ReLU layer ...
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What is the best approach for writing a program to identify objects in a picture then crop them a specific way?
My works quality control department is responsible for taking pictures of our products at various phases through our QC process and currently the process goes:
Take picture of product
Crop the ...
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Applications of CNN for detecting crime from video surveillance cameras
Inspired by this discussion about recognizing human actions, I have found the Fall-Detection project which detects humans falling on the ground from a CCTV camera feed, and which can consider alerting ...
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Aesthetics analysis with deep learning
I'm trying to score video scenes in terms of aesthetics and cinematography features. Basically, how "interesting" a scene or video frame can be for a viewer. Simpler, how attractive a scene is. My ...
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Which loss function is the brain optimizing in order to learn advanced visual skills without expert/human supervision?
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) ...
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