Top Computer Vision Interview Questions and Answers
Computer vision is a rapidly growing field that combines computer science, mathematics and artificial intelligence to enable computers to interpret and understand images and videos. If you’re interviewing for a computer vision job, you may be asked a few questions designed to gauge your knowledge and expertise in the field. In this article, we discuss the most popular computer interview questions and answers.
1 – What is computer vision?
This is a one of the basic questions which you are likely to encounter in any computer vision interview. Before diving into more specific topics, it is important to understand the basic concepts and principles of computer vision. Computer vision involves training computers to recognize, interpret and analyze images and videos using machine learning algorithms.
2 – What are the applications of computer vision?
Computer vision has many practical applications in various industries, such as healthcare, automotive, manufacturing, and entertainment. Some examples include:
- Object recognition and detection
- Facial recognition and emotion analysis
- Medical image analysis
- Autonomous driving
- Augmented and virtual reality
- Quality control and inspection
- Surveillance and security
3 – What are the challenges of computer vision?
Despite the significant progress in computer vision research in recent years, there are still many challenges that need to be addressed, such as:
- Handling large datasets and high-dimensional feature spaces
- Dealing with variations in lighting, pose, and scale
- Overcoming occlusion and clutter in images and videos
- Ensuring robustness and generalization of machine learning models
- Addressing ethical and privacy concerns related to facial recognition and surveillance
4 – What is a convolutional neural network (CNN)?
CNN is a type of deep neural network that has been widely used in computer vision tasks, such as image classification, object detection, and segmentation. It is designed to learn hierarchical representations of images by applying convolution and pooling operations to extract local features and capture spatial relationships between them.
5 – How does a CNN differ from a traditional neural network?
A traditional neural network is a fully connected network that processes inputs by computing a weighted sum of all input features. In contrast, a CNN uses a shared set of filters that convolve over the input image to extract local features and reduce the dimensionality of the feature space. This allows a CNN to learn translational invariance and capture local patterns in the image.
6 – What is transfer learning in computer vision?
Transfer learning is a technique in which a pre-trained neural network is used as a starting point for a new task, rather than training a new network from scratch. This can save a lot of time and computational resources, especially when dealing with limited amounts of labelled data. Transfer learning has been used successfully in various computer vision tasks, such as image classification and object detection.
7 – What are the key components of a computer vision
A computer vision system typically consists of several components, including:
- Image acquisition: Capturing images or video from cameras or sensors.
- Pre-processing: Enhancing and filtering the images to reduce noise and improve contrast.
- Feature extraction: Identifying key features and patterns in the images, such as edges, corners, and textures.
- Machine learning: Training a model to recognize and classify objects based on the extracted features.
- Post-processing: Refining the results and correcting errors, such as false positives or false negatives.
8 – How do you evaluate the performance of a computer vision system?
There are various performance metrics used to evaluate the effectiveness of a computer vision model. One fundamental metric is accuracy, which is the proportion of correct predictions (both true positives and true negatives) among all instances in a given dataset. Other metrics include precision, recall, F1 score, and mean average precision (mAP). Precision measures how many of the predicted positives are actually positive, while recall measures how many of the actual positives are predicted as positive. F1 score is the harmonic mean of precision and recall, and mAP measures the average precision across all recall values.
9 – What is your experience with deep learning frameworks like TensorFlow and PyTorch?
I have worked with both TensorFlow and PyTorch in several computer vision projects. I am comfortable building and training deep learning models with both frameworks and have experience optimizing model performance.
10 – How would you go about improving the accuracy of a computer vision model?
To improve the accuracy of a computer vision model, I would try different techniques such as data augmentation, adjusting the learning rate, and tweaking the architecture of the model. I would also explore pre-trained models and fine-tuning them to my specific use case.
11 – Can you explain the difference between object detection and object segmentation in computer vision?
Object detection refers to identifying the presence and location of objects in an image or video. Object segmentation, on the other hand, involves identifying the boundaries of individual objects within an image or video.
12 – What is your experience with image pre-processing techniques such as normalization and data augmentation?
I have extensive experience with image pre-processing techniques such as normalization, data augmentation, and color space conversion. I understand how these techniques can improve the performance of computer vision models and have used them in several projects.
13 – Can you explain the concept of transfer learning in computer vision?
Transfer learning refers to using a pre-trained model as a starting point for a new computer vision task. By using a pre-trained model, we can leverage the knowledge and experience of the model on a different but related task and fine-tune it to our specific use case.
14 – How do you handle overfitting in computer vision models?
Overfitting occurs when a model is trained too well on a specific dataset, resulting in poor performance on new, unseen data. To handle overfitting in computer vision models, regularization techniques such as dropout, early stopping, and weight decay can be applied. Additionally, data augmentation methods such as rotation, flipping, and scaling can be used to generate more diverse training examples.