Some interview questions and answers for the computer vision field. Part 1 shows some basic interview questions and answers.

Computer Vision Basics Interview Questions & Answers

1. What is Computer Vision?

   Answer: Computer Vision is a field of artificial intelligence that focuses on enabling machines to interpret and understand visual information from the world. It aims to replicate the human visual system by using digital images or videos as input to make decisions or perform tasks.

2. Explain the steps involved in typical Computer Vision tasks.

   Answer: The typical steps in a Computer Vision task are:

   • Image Acquisition: Obtaining images or video frames from cameras or other sources.

   • Preprocessing: This involves tasks like resizing, denoising, and normalizing the images to prepare them for further processing.

   • Feature Extraction: Identifying key characteristics or patterns in the images. This can include edges, corners, textures, etc.

   • Object Recognition or Detection: Identifying and localizing objects within the image.

   • Post-processing: Refining the results, which may include tasks like non-maximum suppression or filtering.

   • Interpretation: Making sense of the results in the context of the specific task.

3. What is the difference between Image Classification and Object Detection?

   Answer:

   • Image Classification is a task where the model predicts a single label or class for an entire image. It doesn’t provide information about the location of objects within the image.

   • Object Detection, on the other hand, not only identifies the objects in an image but also provides their precise location by drawing bounding boxes around them.

4. What is Convolution in Convolutional Neural Networks (CNNs)?

   Answer: Convolution is a mathematical operation that combines two functions to produce a third function. In the context of CNNs, convolution involves sliding a filter (also known as a kernel) over the input image to extract features like edges, corners, textures, etc. This operation is fundamental to CNNs as it allows them to automatically learn relevant features from the data.

5. What is the purpose of Pooling in CNNs?

   Answer: Pooling is used in CNNs to downsample the spatial dimensions of the feature maps while retaining the most important information. It helps reduce the computational complexity and the number of parameters in the network, making it more manageable. Common pooling techniques include max pooling and average pooling.

6. What is the purpose of Activation Functions in neural networks?

   Answer: Activation functions introduce non-linearity into the model, allowing it to learn and approximate complex, non-linear relationships in the data. Without activation functions, the entire network would behave like a linear model, which is not suitable for tasks like image recognition.

7. What is Transfer Learning in the context of Computer Vision?

   Answer: Transfer learning is a technique where a pre-trained neural network, typically on a large dataset, is used as a starting point for a new task. Instead of training a model from scratch, the existing knowledge from the pre-trained network is fine-tuned on a smaller dataset specific to the new task. This is particularly useful when the new task has limited data.

8. What are some popular deep learning frameworks used for Computer Vision?

   Answer: Common deep learning frameworks for Computer Vision include TensorFlow, PyTorch, Keras, and OpenCV.