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About this interview
A technical interview on Computer Vision Tasks, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain object detection approaches (two-stage R-CNN family vs one-stage YOLO/SSD) and the role of anchors and NMS
Describe semantic vs instance vs panoptic segmentation and representative architectures
Define detection/segmentation metrics (IoU, mAP) and how they're computed
Reason about real-time vs accuracy trade-offs in a vision pipeline
Topics covered
IoU and metricsNMS and post-processingAnchors and region proposalsTwo-stage detectorsDetection paradigmsOne-stage detectorsFeature pyramids and multi-scaleAnchor-free detectionSegmentation typesSegmentation architecturesSegmentation metricsInstance segmentationMulti-scale and real-world challengesTraining challenges
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In object detection, what does Intersection over Union actually measure, and why is it the right metric for comparing a predicted bounding box to a ground-truth box?
“Compare one-stage detectors like SSD and YOLO against two-stage detectors like Faster R-CNN. What does each family trade for its advantage?
“How does Mask R-CNN extend Faster R-CNN to produce instance segmentation masks? What head does it add and how does it interact with the detection head?