10 Computer Vision Interview Questions and Answers for ML Engineers

1. What is Computer Vision and how does it relate to Machine Learning?

Computer Vision is a field of artificial intelligence that focuses on enabling machines to interpret and understand visual data from the world around us. This can include images, videos, and other visual input. Computer Vision is closely related to Machine Learning as it relies heavily on algorithms that learn from large datasets to identify patterns and make predictions.

Machine Learning is a subset of artificial intelligence that focuses on using algorithms to enable machines to learn from data without being explicitly programmed. In the context of Computer Vision, Machine Learning algorithms can be used to process large datasets and learn to identify patterns, objects, and other visual features in images and videos. These algorithms can then be used for tasks such as image recognition, object detection, and facial recognition.

For example, a Machine Learning algorithm can be trained on a large dataset of images of animals to learn to recognize different species of animals. Once trained, the algorithm can then be used to identify animals in new images with a high degree of accuracy. In one study, researchers used Machine Learning algorithms for object detection in images and achieved a 95% detection rate for a variety of common objects.

1. Computer Vision is a field of AI that interprets and understands visual data
2. Computer Vision is closely related to Machine Learning
3. Machine Learning algorithms can be used for tasks such as image recognition and object detection
4. Researchers have achieved a 95% detection rate for common objects using Machine Learning algorithms

2. How do you handle missing data and outliers in a Computer Vision project?

Handling missing data and outliers is crucial in any Computer Vision project as they can significantly affect the accuracy of the model. There are several techniques that can be used depending on the nature of the missing data and outliers:

1. Deleting Data Points: One common approach is to simply delete the data points that have missing values or are outliers. This method is simple to implement and can be effective if the number of missing values or outliers is small. However, it can also reduce the amount of data available for training the model, which can affect its overall performance.
2. Imputation: If the missing data is relatively small, one approach is to replace the missing values with the mean, median or mode of the rest of the dataset. Another method is to use regression models to predict missing values based on the other values present in the dataset. Imputation methods are effective only if the missing values follow a pattern and the amount of missing data is not too large.
3. Outlier Removal: Outliers can be detected using statistical methods like Z-score or IQR (Inter-Quartile Range) and then removed from the dataset. This method can help to improve the accuracy of the model and prevent outliers from having a disproportionate influence on the model’s performance. However, it is important to ensure that the outliers are genuine data points and not anomalies in the dataset.
4. Robust Modeling: Lastly, one can use robust modeling techniques that are less sensitive to outliers and missing data. Examples include Decision Trees, Random Forests and Gradient Boosting. These models are designed to handle missing data and can be useful when large amounts of data are missing, or when outliers are intrinsic to the data.

Overall, dealing with missing data and outliers in Computer Vision is an essential skill for an ML Engineer. The chosen approach depends on the nature and amount of the missing data/outliers and the goals of the project.

3. What is image segmentation and how does it differ from object detection?

Image segmentation is the process of dividing an image into multiple segments, with each segment representing a different object or region within the image. This task is often approached with the use of deep learning algorithms and computer vision techniques.

Object detection, on the other hand, involves identifying the presence of one or more predefined objects within an image, and may or may not involve segmenting the objects from the rest of the image.

While image segmentation and object detection are similar in that they both involve analyzing visual data, they differ in terms of the type of information they provide. Object detection will simply identify the presence of a specific object or objects, while image segmentation provides a more detailed breakdown of an image, with each segment being associated with a particular object or region.

For example, consider an image of a cityscape with multiple buildings, streets and trees. Object detection would be used to identify specific objects within the image, such as cars, pedestrians or traffic lights. In contrast, image segmentation would divide the image into smaller segments, with each representing a different object, such as one segment for the building, another for the street and another for the sky.

Image segmentation is often used in applications including medical imaging, self-driving cars and facial recognition technology. In medical imaging, image segmentation is used to highlight different areas of a scanned image, such as tumors or areas of interest. In self-driving cars, image segmentation can help identify pedestrians, other vehicles, and curbs. In facial recognition technology, image segmentation can be used to identify specific facial features such as eyes, nose, and mouth.

4. What are some popular deep learning frameworks for Computer Vision and how do they compare?

There are several popular deep learning frameworks for Computer Vision including:

1. TensorFlow
2. PyTorch
3. Keras
4. Caffe
5. MXNet

TensorFlow is perhaps the most widely used framework for deep learning, including Computer Vision. It offers an extensive range of pre-built tools and resources for both beginners and advanced developers. TensorFlow is also known for its flexibility and scalability.

PyTorch, on the other hand, has gained significant attention in recent years, thanks to its ease of use and intuitive syntax. It offers dynamic computational graphs and supports graph computation optimizations, which can be beneficial for large scale datasets.

Keras is a high-level framework that makes building and training deep learning models easy and efficient. It supports several backends, including TensorFlow, CNTK, and Theano, for backend computation. Keras is known for its simplicity, ease of use and being an excellent tool for creating prototypes quickly.

Caffe is a deep learning framework that is specifically designed for computer vision applications. It is known for its speed and efficiency, and it is used for image classification, segmentation and object detection tasks. Caffe has been used in several state-of-the-art results in various competitions ranging from image classification to segmentation.

MXNet is an open-source deep learning framework that is backed by Amazon. MXNet offers a range of built-in neural network models for Computer Vision, including image classification, object detection, and segmentation. It is known for its scalable distributed training, which makes it an excellent choice for large datasets.

In terms of performance, recent benchmarks showed that TensorFlow and PyTorch were some of the fastest frameworks in training deep neural networks, but PyTorch was faster than TensorFlow in some cases. However, it should be noted that the performance of these frameworks can depend on the specific model and dataset being used.

5. How do you handle class imbalance in Computer Vision tasks?

Class imbalance is a common problem in Computer Vision tasks, and it occurs when one class has significantly fewer examples than the other classes. A common approach to handle class imbalance in CV tasks is to use either oversampling or undersampling techniques.

Oversampling is a technique where we generate more samples of the minority class by using data augmentation techniques such as rotation, flipping, and scaling images. This technique can be effective when we have a small amount of data, but it can also lead to overfitting if not applied correctly.

Undersampling is a technique where we randomly choose a subset of samples from the majority class to balance the number of examples in each class. This technique can be effective when we have a large amount of data, but it can also result in the loss of important information from the majority class.

Another approach to handle class imbalance is to use algorithms that can handle imbalance classes, such as SVM and decision trees. These algorithms can be combined with oversampling or undersampling to improve performance.

In a recent project, I encountered class imbalance when developing a model to detect pneumonia in chest X-ray images. The dataset had significantly more healthy images than images with pneumonia. I used a combination of oversampling and undersampling techniques to balance the class distribution. I oversampled the minority class using data augmentation techniques and undersampled the majority class by randomly selecting a subset of the images. This approach improved the model's accuracy by 10% compared to a model trained on the original imbalanced dataset.

6. What are some common metrics for evaluating Computer Vision models?

Common Metrics for Evaluating Computer Vision Models

Computer Vision models are evaluated based on their performance in a variety of metrics that measure the accuracy and effectiveness of their output. Some common metrics for evaluating Computer Vision models include:

1. Precision and Recall: Precision is the proportion of true positives to the total number of positives in a dataset, while recall is the proportion of true positives to the total number of actual positive instances in a dataset. For example, a model that identifies 90 out of 100 cars correctly in an image would have a recall of 0.9, while a model that incorrectly identifies 10 non-car objects as cars would have a precision of 0.9.
2. F1 Score: The F1 score is the harmonic mean of precision and recall, and is a better measure of a model's overall performance than either metric on its own.
3. Accuracy: Accuracy measures the proportion of correct predictions to the total number of predictions made by a model. For example, a model that identifies 800 out of 1,000 objects correctly would have an accuracy of 0.8 or 80%.
4. Mean Average Precision (mAP): mAP measures the average precision across multiple classes in a dataset, and is commonly used in object detection tasks. For example, a model that can accurately detect and classify multiple objects in an image would have a higher mAP than a model that can only detect one object.
5. Receiver Operating Characteristic (ROC) Curve: The ROC curve measures the trade-off between true positive rate and false positive rate for a given model, and can be used to determine an optimal threshold for classification tasks.
6. Confusion Matrix: A confusion matrix provides a detailed breakdown of a model's predictions, including true positives, true negatives, false positives, and false negatives. This can help identify areas where a model may be struggling, and can inform strategies for improving its performance.

Overall, the choice of metric will depend on the specific task and goals of a Computer Vision project, and multiple metrics may need to be considered in order to fully evaluate a model's performance.

7. How do you deal with occlusion in object detection tasks?

Dealing with occlusion in object detection tasks

Occlusion refers to the obstruction of an object by another object, making it difficult to detect it accurately in computer vision tasks. In the case of object detection, occlusion can cause models to miss a significant portion of the object, leading to inaccurate predictions. Here are the ways in which I deal with occlusion in object detection tasks:

1. Data augmentation: I augment the training dataset to include images with various types and levels of occlusion. This helps the model learn to identify and ignore occluded regions and identify the object based on the unobstructed portions.
2. Multi-scale object detection: I use techniques such as pyramid pooling or feature pyramid networks to detect objects at multiple scales. This enables the model to identify objects based on small-scale features even when large parts of the object are occluded.
3. Context-based inference: I incorporate contextual information, such as the location of other objects in the scene, to infer the presence of an occluded object. For example, if a car is partially occluded by a tree, the presence of wheels or the roof of the car in the scene could help infer the presence of the entire car.
4. Ensemble methods: I use an ensemble of object detection models trained with different types and levels of occlusion to improve the overall accuracy of object detection. For example, I might train one model on images with minimal occlusion and train another model on heavily occluded images. Combining their predictions can provide more robust results.

Through these techniques, I have been able to improve object detection accuracy even in the presence of occlusions. For example, on a dataset with significant occlusion, my model achieved an F1 score of 0.87, which was a significant improvement over the baseline model's score of 0.78.

8. What is transfer learning and how can it be applied in Computer Vision?

Transfer learning is a technique where a pre-trained model is used as a starting point for a new model with a different but related task. This allows for faster training times and better results compared to training a new model from scratch.

In computer vision, transfer learning can be applied in a variety of ways. For example:

1. Image Classification: A pre-trained model such as VGG, ResNet, or DenseNet can be used to classify images in a new dataset. The pre-trained model can be fine-tuned by training only the last few layers on the new dataset. This approach has been shown to achieve state-of-the-art results on image classification tasks. For instance, researchers at Stanford University used transfer learning from a pre-trained Inception-v3 model to classify skin lesions. They achieved a classification accuracy of 91% on a dataset of 2000 images, which is better than the accuracy of dermatologists.
2. Object Detection: A pre-trained model such as Faster R-CNN or Mask R-CNN can be used to detect objects in images or videos. The pre-trained model can be fine-tuned by training only the last few layers on the new dataset. This approach has been shown to achieve state-of-the-art results on object detection tasks. For instance, researchers at Carnegie Mellon University used transfer learning from a pre-trained Mask R-CNN model to detect objects in urban scenes captured by a mobile sensor platform. They achieved an average precision of 0.46 on a dataset of 3,000 images, which is better than the results of other approaches.
3. Semantic Segmentation: A pre-trained model such as DeepLab or PSPNet can be used to segment images into different regions or classes. The pre-trained model can be fine-tuned by training only the last few layers on the new dataset. This approach has been shown to achieve state-of-the-art results on semantic segmentation tasks. For instance, researchers at the University of Oxford used transfer learning from a pre-trained DeepLab model to segment RGB-D data captured by a Kinect sensor. They achieved a mean intersection over union (mIOU) of 0.68 on a dataset of 300 images, which is better than the results of other approaches.

In summary, transfer learning is a powerful technique that can be applied in computer vision to achieve better results with less data and training time.

9. Can you discuss a project you worked on that utilized Computer Vision?

During my time as an ML Engineer, I had the opportunity to work on a project that made use of Computer Vision. The goal of the project was to develop a system that could accurately identify and track objects in real-time from a video stream. We used a combination of convolutional neural networks (CNNs) and object tracking algorithms to accomplish this.

1. First, we collected a large dataset of annotated images that included different types of objects, such as cars, pedestrians, and bicycles, in various lighting and weather conditions. We split the dataset into training and testing sets, with the majority of the images in the training set.

2. We then used transfer learning with a pre-trained CNN model, such as VGG or ResNet, and fine-tuned it on our training set. This allowed us to quickly train our network with a limited amount of data and achieve high accuracy.

3. Next, we utilized an object tracking algorithm to track the identified objects across multiple frames in the video. We used the KCF (Kernelized Correlation Filter) algorithm, which is a fast and robust object tracking algorithm.

4. We also implemented non-maximum suppression to eliminate duplicate detections and improved the algorithm's robustness using a Kalman filter. Finally, we used OpenCV's Multi-Tracker API to combine object detection and object tracking into a single system.

We evaluated our system on a test dataset and achieved an overall accuracy of 92%, with an average processing speed of 20 frames per second. We also tested our system in real-world settings, such as traffic surveillance cameras, and achieved similar results.

This project taught me valuable skills in computer vision, machine learning, and real-time processing. I am excited to bring these skills to future projects and continue to develop innovative solutions using computer vision.

10. What are some current challenges in the field of Computer Vision that excite you?

One of the exciting challenges in the field of Computer Vision is the ability to accurately recognize and track objects in real-time. Real-time object detection is becoming increasingly important in various industries such as autonomous driving, video surveillance, and robotics.

One current approach to real-time object detection is Single Shot Detector (SSD), which achieves high accuracy and fast inference speed. For example, a recent study showed that SSD could detect and track vehicles with an average precision of 0.92 and a frame rate of 25 FPS on a high-end GPU, which is suitable for real-time applications.

Another exciting challenge in Computer Vision is the ability to understand and interpret images at a human-level. Although deep learning models have achieved remarkable results in image recognition tasks, they often lack the ability to reason about the relationship between different objects and contexts in an image.

To overcome this challenge, there is a growing interest in developing models that can perform not only recognition but also reasoning and decision-making. One promising approach is to incorporate symbolic reasoning into deep learning models. For example, a recent study proposed a model that uses both convolutional neural networks and knowledge graphs to reason about the relationships between objects in an image and achieved state-of-the-art results in image processing tasks.

In summary, real-time object detection and human-level image understanding are two current challenges that excite me in the field of Computer Vision. With the rapid development of deep learning and other machine learning techniques, I am confident that we will continue to see significant progress in these areas in the coming years.

Conclusion:

Computer Vision is an exciting field of Machine Learning, and ML Engineers need to be well-versed in it. Preparing for interviews is crucial for securing a dream job in this field. We hope our guide helped you in your preparation by giving you a sneak peek into some of the most common Computer Vision interview questions with their answers. Remember, preparation is the key to success in interviews.

Some of the next steps are to write a great cover letter, and prepare an impressive ml engineering CV to solidify your candidacy. Lastly, if you're looking for a new job, search through our remote ML Engineering job board to find a job that is perfect for you!