Easy interview questions in Computer Vision, page 2
GAN architecture: generator and discriminator roles
Generator maps noise to fake samples, discriminator classifies real versus fake, they train as a two-player game until samples fool the discriminator.
Diffusion forward and reverse processes
Forward process gradually adds Gaussian noise until data is pure noise; reverse process learns to denoise step by step; the network predicts the noise added at each timestep.
Semantic, instance, and panoptic segmentation
Semantic labels every pixel by class without separating objects; instance separates individual objects but may skip background; panoptic unifies both, labeling stuff and distinct thing instances.
Designing a baseline Visual Question Answering model
Encode the image with a CNN, encode the question with an RNN or embedding, fuse the two vectors, and classify over a fixed answer vocabulary.
Transfer learning from ResNet50 on small data
Replace the final classification head with one sized to your classes, freeze the pretrained convolutional backbone as a feature extractor, train the new head, then optionally fine-tune top blocks at a low…
Leveraging unlabeled data with limited labels
Pretrain a representation on the million unlabeled images via self-supervision, then fine-tune on the 1,000 labels; or use pseudo-labeling and consistency regularization.
How do you build an HDR image from bracketed exposures?
Align frames, recover the camera response function, merge to a linear radiance map weighted by exposure, then tone map for display.
Outline the classic image stitching pipeline.
Detect and match features like SIFT, estimate a homography with RANSAC, warp and blend with multiband or feathering.
What data augmentations help small image datasets?
Apply label-preserving transforms like flips, crops, rotation, color jitter, and mixing to enlarge effective data and reduce overfitting.
Precision vs recall in object detection.
Precision is fraction of detections that are correct, recall is fraction of true objects found; prioritize recall for safety-critical detection, precision when false alarms are costly.
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