I knew the gist of it, assigning a class label to every pixel in an image rather than just drawing a bounding box around objects.
Start with a clear, concise definition of semantic segmentation, then explain the typical architecture (e.g., encoder-decoder with skip connections) and how it assigns a class label to each pixel. Emphasize the role of loss functions (e.g., cross-entropy) and mention common evaluation metrics like IoU. Tailor the explanation to Uber's scale by discussing trade-offs in model efficiency and real-time inference.
Pro tip: Connect semantic segmentation to Uber's use cases like autonomous driving or map enhancement, showing you understand the business impact. Also, mention recent advancements like transformers or efficient architectures (e.g., MobileNet) to demonstrate up-to-date knowledge.
Explain that it's a computer vision task where each pixel in an image is classified into a predefined category, unlike object detection which uses bounding boxes.
Outline the encoder-decoder structure: the encoder extracts features and reduces spatial dimensions, while the decoder upsamples to restore resolution, often with skip connections (e.g., U-Net) to retain fine details.
Mention that models are trained with pixel-wise cross-entropy loss, sometimes combined with Dice loss for class imbalance. Discuss how ground truth masks are used.
Cover metrics like IoU and pixel accuracy, and trade-offs between accuracy and inference speed, especially for real-time applications at Uber.
Connect to Uber's needs: autonomous vehicle perception, mapping, or food delivery image analysis, highlighting scalability and efficiency considerations.
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