computer vision

CNNs remain foundational even as transformers rise, and this checks whether you grasp why convolution suits images. What interviewers reward is parameter sharing and local connectivity, what pooling buys, and how the receptive field grows. Here is the answer.

Boxes and labels, not a single tag per image. The signal is picking two-stage versus one-stage from your latency and accuracy budget, knowing what NMS and anchors really do, and reporting mAP correctly. Here is the answer interviewers score highest.

Semantic labels every pixel by class; instance pulls each object apart. The signal is knowing that the first cannot count overlapping objects, why Mask R-CNN adds a mask branch on RoIAlign, and what RoIAlign fixed. Here is the answer.

Detecting and boxing objects at scale reduces to one driving tradeoff plus a handful of CV specifics most candidates fumble: the detector family, NMS, focal loss, and why mAP rather than accuracy is the metric. Here is the design.

Millions of landmarks exist, most with only a handful of photos, and the next photo could show something not in your catalog at all. A flat classifier collapses on the long tail and never says 'I don't know.' The interview is embeddings plus retrieval plus a confident refusal.