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Example Computer Vision flashcards
What is the core task of computer vision?
Enable computers to understand and extract meaningful information from visual data (images/videos) the way humans perceive the world.
What is a digital image at its most basic level?
A 2D grid of numbers called pixels, where each number represents intensity (grayscale) or color values (RGB: red, green, blue channels).
Why do we apply filters/kernels to images in computer vision?
To detect patterns like edges, textures, or features by computing weighted sums of pixel neighborhoods, transforming raw pixel data into meaningful signals.
What is convolution in image processing?
A mathematical operation that slides a small filter (kernel) over an image, computing element-wise products and summing results at each position to detect local features.
How do Convolutional Neural Networks (CNNs) learn features automatically?
By stacking convolutional layers where early layers learn low-level features (edges), middle layers learn mid-level features (textures, shapes), and deeper layers learn high-level semantic features (objects).
What does pooling do in a CNN and why?
Pooling (max or average) reduces spatial dimensions by summarizing regions, making the network more efficient, robust to small translations, and focusing on dominant features.
What is image segmentation and how does it differ from classification?
Segmentation assigns a class label to every pixel (pixel-level prediction), while classification assigns one label to the entire image. Segmentation provides spatial localization.
How does object detection combine classification and localization?
It identifies what objects are present (classification) and where they are (bounding boxes or keypoints), typically using region proposals or anchor-based methods like YOLO or R-CNN.
What is the purpose of transfer learning in computer vision?
Reuse pre-trained weights from models trained on large datasets (ImageNet) to solve new tasks with limited data, avoiding training from scratch and improving efficiency.
What is the intuition behind attention mechanisms in vision models?
Allow models to focus on important image regions or features dynamically; the network learns to weight different parts of the input based on relevance to the task.
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