10+ Computer Vision Engineer Interview Questions and Answers

Yes, MATLAB can be used to train models for image classifications.

• MATLAB provides tools like Image Processing Toolbox and Deep Learning Toolbox for training models.

• You can use pre-trained models like AlexNet, VGG-16, etc. for image classification tasks.

• MATLAB allows for data augmentation, transfer learning, and fine-tuning of models for better performance.

I would approach the problem by collecting a dataset of images containing plastic bottles, preprocessing the images, selecting a suitable model architecture, training the model, and evaluating its performance.

• Collect a dataset of images containing plastic bottles and label them accordingly

• Preprocess the images by resizing, normalizing, and augmenting them to improve model performance

• Select a suitable model architecture such as Convolutional Neural Network (CNN) for image clas...read more

1. YOLO v4 is a complex architecture for object detection. 2. SIFT is invariant to scale and angle due to its keypoint detection and descriptor extraction techniques. 3. GANs and VAEs differ in their approach to generative modeling.

• YOLO v4 is a state-of-the-art object detection architecture that uses a deep neural network with multiple layers.

• SIFT (Scale-Invariant Feature Transform) is invariant to scale and angle because it uses a scale-space extrema detection algorithm and ...read more

To rotate an image in Python, use the Pillow library's rotate() method.

• Import the Image module from the Pillow library

• Open the image using the open() method

• Use the rotate() method to rotate the image by the desired angle

• Save the rotated image using the save() method

CNN is used for image recognition while RNN is used for sequence data like text or speech.

• CNN is Convolutional Neural Network, used for image recognition tasks.

• RNN is Recurrent Neural Network, used for sequence data like text or speech.

• CNN has convolutional layers for feature extraction, while RNN has recurrent connections for sequential data processing.

• CNN is good at capturing spatial dependencies in data, while RNN is good at capturing temporal dependencies.

• Example: CNN can...read more

The loss function for VAE is a combination of a reconstruction loss and a regularization loss.

• The reconstruction loss measures the difference between the input and the output of the VAE.

• The regularization loss encourages the latent space to follow a prior distribution, typically a Gaussian distribution.

• The total loss is the sum of the reconstruction loss and the regularization loss.

• Commonly used reconstruction loss functions include mean squared error (MSE) and binary cross-e...read more

Activation functions are used to introduce non-linearity into neural networks, allowing them to learn complex patterns and relationships.

• Activation functions help neural networks to learn complex patterns and relationships by introducing non-linearity.

• They help in controlling the output of a neuron, ensuring that it falls within a desired range.

• Common activation functions include ReLU, Sigmoid, Tanh, and Leaky ReLU.

• Without activation functions, neural networks would simply be...read more

Yes, Logloss function is differentiable.

• Logloss function is differentiable as it is a smooth and continuous function.

• The derivative of Logloss function can be calculated using calculus.

• Differentiability is important for optimization algorithms like gradient descent to converge smoothly.

• Example: The derivative of Logloss function for binary classification is (predicted probability - actual label).

Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.

• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to overshoot the optimal weights, while a low learning rate can result in slow convergence.

• Common learning rate values are 0.1, 0.01, 0.001, etc.

• Learning rate can be adjusted during training using techniques like learning rate sche...read more

Breadth first search problem

• BFS is a graph traversal algorithm that visits all the vertices of a graph in breadth-first order

• It uses a queue to keep track of the nodes to be visited next

• BFS can be used to find the shortest path between two nodes in an unweighted graph

The Pythagorean Theorem states that in a right-angled triangle, the square of the length of the hypotenuse is equal to the sum of the squares of the lengths of the other two sides.

• In a right-angled triangle, let the lengths of the two shorter sides be a and b, and the length of the hypotenuse be c.

• According to Pythagorean Theorem, a^2 + b^2 = c^2.

• For example, in a triangle with sides of length 3, 4, and 5, 3^2 + 4^2 = 5^2.