Computer Vision Fundamentals Questions

Core concepts and methods in computer vision with an emphasis on both traditional image processing and modern deep learning approaches. Candidates should understand how images are represented as matrices or tensors, common preprocessing steps and augmentation techniques to improve generalization, and fundamentals of convolutional neural networks including convolution operations, receptive fields, pooling, and normalization. Familiarity with common vision tasks such as image classification, object detection, semantic and instance segmentation, and key model design patterns is expected. Candidates should know common vision architectures and families such as residual networks and Visual Geometry Group style networks, the role of pretrained models and transfer learning, how to fine tune models for new tasks, and practical tooling including image processing libraries and deep learning frameworks for training and inference. Evaluation may include trade offs between accuracy, latency, and resource usage for deployment.