6 Computer Vision Interview Questions and Sample Answers

If you’re pursuing a career as a computer vision engineer or a similar role, preparing to answer computer vision interview questions can help you make an excellent impression. The person conducting your interview will most likely want to understand your technical knowledge, how you apply computer vision strategies to real-world problems, and what professional experiences you’ve had in the computer vision field. 

While preparing for your interview, it can be helpful to think about the questions the interviewer will ask and how your past experiences shape your problem-solving strategies and thought processes around overcoming challenges in computer vision. Explore six computer vision interview questions a recruiter may ask you, insight into what the interviewer wants to know, and an example of what you might say. 

Fundamental computer vision interview questions

You can prepare to answer various computer vision interview questions, including those about your technical proficiency in machine learning, image processing, feature detection, and neural networks. More advanced topics that your interviewer may ask about include facial recognition or real-time object detection. Explore a few examples of computer vision interview questions you may face in an interview. 

1. What is the difference between image recognition and object detection?

What they really want to know: Your interviewer wants to assess your understanding of basic computer vision tasks. This question can take the form of comparing any two basic concepts in computer vision. 

Example answer: Image recognition, also called image classification, sorts images into classes or categories, while object detection finds instances of each object and determines the object’s location in an image. For example, image recognition would sort all images that include a dog, and object detection would categorize each dog that appears in all of the images combined, as well as any other objects specified, such as cats, trees, and dog toys also found in the image. 

A third technique, image segmentation, can go further than object detection to sort images within a class by semantic categories at the pixel level. 

2. Can you explain what a convolutional neural network (CNN) is and how it works?

What they really want to know: Your interviewer wants to evaluate your familiarity with key models used in computer vision. You should also prepare to answer questions about other computer vision models such as ResNet, YOLO, vision transformers, and stable diffusions. 

Example answer: A convolutional neural network (CNN) is an artificial intelligence model that specializes in processing images and 3D information. It contains hidden layers called convolutional layers that break images down to the pixel level to extract and analyze the data found there before pooling the information back together until it understands the entire image. The more hidden layers inside a CNN, the more complex the analysis the model can perform. CNNs are also often called deep convolutional neural networks due to the many hidden layers in the model that add to its image processing capabilities. 

Read more: 21 Examples of Computer Vision Applications Across Industries

Advanced computer vision interview questions

Depending on the role within computer vision you’re interviewing for, you may need to answer some questions on more advanced computer vision techniques. Consider a few sample questions to demonstrate what advanced computer vision interview questions may look like. 

1. How is a vision transformer different from a CNN?

What they really want to know: The interviewer wants to test your knowledge of specific algorithms and their application. Beyond how these models work, the interviewer wants to know how you would apply different models to different problems. 

Example answer: A vision transformer differs from a convolutional neural network because it uses a transformer rather than a neural network. Transformers are commonly used in natural language processing, such as ChatGPT (“GPT” stands for generative pre-trained transformer). Applying transformer technology to computer vision provides a more accurate, lightweight, and fast image processing algorithm for tasks like facial recognition. At the same time, a vision transformer requires more data to train, so a convolutional neural network can work more efficiently with smaller data sets. 

2. Explain the key challenges of computer vision in autonomous vehicles

What they really want to know: The interviewer wants to check your understanding of advanced models and creative problem-solving in AI.

Example answer: One of the most pressing challenges of implementing computer vision in autonomous vehicles is the unpredictable and uncontrolled environment they need to function within. Autonomous vehicles require object perception, detection, tracking, motion planning, and a comprehensive decision-making process that evaluates the visual field and acts accordingly. A self-driving car needs to be aware of the road surface, which objects are moving and which are still, lanes of driving, and more, all in varying levels of light and shadow. The main challenge is creating comprehensive, complex algorithms to manage these factors. 

Practical application and problem-solving

Your future employer may want to know how you will manage hypothetical situations to understand your ability to apply practical knowledge and solve problems. Explore some more examples of problem-solving computer vision interview questions you might face. 

1. How would you improve a real-time object classification system for accuracy and speed?

What they really want to know: The interviewer is trying to gauge your problem-solving skills and practical application of knowledge.

Example answer: To improve a real-time object classification system, you could start by determining the best architecture for real-time processing, such as using a YOLO or SSD model. You can then look for ways to reduce the complexity of the model, which can result in a slightly less precise but still accurate model with an improved speed. Sometimes, you can use a strategy of optimizing hardware and software to manage pre-processing and computation speed. Sometimes, you might reach for more advanced solutions like hyperparameter tuning or using augmented data for training. 

2. Given an imbalanced data set, how would you train a model to accurately classify images?

What they really want to know: The interviewer wants to learn about your experience, resilience, and problem-solving strategy. Your interviewer may ask you about a type of computer vision project that uses tools or theories similar to the work you will complete in your new role. 

Example answer: Imbalanced data sets are common when working with real-world problems. Depending on the data set, you can use oversampling or undersampling to balance the distribution of your data. You can use data augmentation techniques to create synthetic data in underrepresented classes, random sampling, and other methods to use smaller samples within the overrepresented classes to match the underrepresented ones. 

How to prepare for a technical interview

To prepare for a technical interview, draw on your training and skills. You may be asked to demonstrate your technical abilities, such as completing a real-time coding test or presenting a technical topic. While the specific format of your interview will be different depending on the role and company you’re applying with, it’s a good idea to brush up on the fundamentals of your field and review the technical concepts relevant to the position you want to land. 

Review your resume or CV and consider the projects you’ve worked on. You’ll want to be ready to talk about how you contributed to the success of a group project or how your unique approach to a problem was successful. A great way to review your work is to practice interviewing with a friend or colleague. This can help you reduce anxiety about sitting for an interview and help you practice formulating confident responses. 

Prepare for your computer vision interview on Coursera

Reviewing computer vision interview questions is a great way to prepare for an interview as a systems developer, computer network architect, or computer vision engineer. If you want to brush up on concepts, strengthen computer vision skills, or add a credential to your resume to set yourself apart from other applicants, you can start today on Coursera.

Consider a Specialization like First Principles of Computer Vision Specialization offered by Columbia University to learn or review skills like 3D reconstruction, perception, object recognition, features and boundaries, and camera and imaging. You might also consider a Professional Certificate like MathWorks Computer Vision Engineer Professional Certificate for a career credential you can share with potential employers.