More in AI & ML — page 11
Why learn detection and description jointly like SuperPoint?
WHAT IT TESTS: learned versus classical keypoints. OUTLINE: a shared backbone jointly optimizes detection and description for matching, sharing computation and learning data-driven robustness instead of hand-crafted heuristics.
Feature detector vs feature descriptor.
WHAT IT TESTS: keypoint pipeline fundamentals. OUTLINE: a detector finds where interesting points are, a descriptor encodes the local appearance around each so points can be matched. RED FLAG: conflating the two or thinking one method only does detection.
How is an HDR radiance map constructed from exposures?
WHAT IT TESTS: HDR radiance recovery. OUTLINE: recover the inverse camera response function from corresponding pixels, linearize each exposure to radiance, then merge with confidence weights into a floating-point radiance map.
Design drift detection and retraining for a CV model.
WHAT IT TESTS: MLOps drift handling. OUTLINE: monitor input and prediction distributions plus delayed labels, detect data and concept drift, trigger an automated retrain-validate-deploy pipeline with versioning and rollback.
Design a cashierless retail checkout vision system.
WHAT IT TESTS: end-to-end CV system design. OUTLINE: multi-camera coverage, person tracking and re-ID, product detection plus action recognition for take or return, sensor fusion, edge-cloud split, and failure handling.
CPU vs GPU vs Edge TPU for inference.
WHAT IT TESTS: inference hardware trade-offs. OUTLINE: CPU is flexible but slow, GPU offers massive parallelism at high power, Edge TPU gives efficient low-power int8 inference but is constrained; choose by latency, power, cost, and model fit.
How do you train and evaluate on imbalanced defect data?
WHAT IT TESTS: handling class imbalance. OUTLINE: resampling, class weighting, focal loss, and anomaly framing for training; evaluate with precision, recall, PR-AUC, and F-beta, not accuracy.
How do you speed up a slow detection model?
WHAT IT TESTS: inference optimization techniques. OUTLINE: quantization, pruning, distillation, lighter backbones, and resolution or batching tweaks, each trading some accuracy or effort for speed.
Precision vs recall in object detection.
WHAT IT TESTS: precision and recall trade-offs. OUTLINE: 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.
What data augmentations help small image datasets?
WHAT IT TESTS: regularization through augmentation. OUTLINE: apply label-preserving transforms like flips, crops, rotation, color jitter, and mixing to enlarge effective data and reduce overfitting.
How does focus stacking build an all-in-focus image?
WHAT IT TESTS: extended depth of field via focus stacking. OUTLINE: align frames, measure local sharpness as a focus metric, select or blend the sharpest source per region, then composite.
Why merge a burst instead of one long low-light exposure?
WHAT IT TESTS: burst denoising fundamentals. OUTLINE: short frames avoid motion blur and clipping while averaging cuts noise; align frames and merge robustly, rejecting misaligned regions to avoid ghosting.
How does a plenoptic camera enable post-capture refocus?
WHAT IT TESTS: light field imaging and refocusing. OUTLINE: light field records ray direction plus position via a microlens array, refocusing shears and integrates the 4D data to choose a virtual focal plane.
Compare Gray World and White Patch white balance.
WHAT IT TESTS: classic color constancy methods. OUTLINE: Gray World assumes average scene color is gray, White Patch assumes the brightest pixel is white, both fail on dominant colors or clipping; learning predicts illuminant from data.
How does smartphone Portrait Mode produce bokeh?
WHAT IT TESTS: depth estimation plus synthetic rendering. OUTLINE: estimate per-pixel depth via dual-pixel or stereo or learning, segment the subject, then apply depth-dependent blur.
Outline the classic image stitching pipeline.
WHAT IT TESTS: feature-based image stitching. OUTLINE: detect and match features like SIFT, estimate a homography with RANSAC, warp and blend with multiband or feathering.
How do you build an HDR image from bracketed exposures?
WHAT IT TESTS: HDR imaging pipeline basics. OUTLINE: align frames, recover the camera response function, merge to a linear radiance map weighted by exposure, then tone map for display.
How do BYOL and Barlow Twins avoid representation collapse?
WHAT IT TESTS: self-supervised learning and collapse avoidance. OUTLINE: collapse is embeddings shrinking to a constant or low-rank subspace; BYOL uses predictor plus momentum target plus stop-gradient, Barlow Twins decorrelates feature dimensions.
How does MAML's inner and outer loop work?
WHAT IT TESTS: meta-learning and bi-level optimization. OUTLINE: inner loop does task-specific gradient steps from shared init, outer loop updates the init for fast adaptability via second-order gradients.
Prototypical Networks for few-shot classification
WHAT IT TESTS: metric-based few-shot learning. OUTLINE: an encoder embeds support examples, each class prototype is the mean embedding of its support examples, and a query is classified by nearest prototype using a distance like Euclidean via softmax.