Design an active learning loop for detection
WHAT IT TESTS: active learning system design. OUTLINE: seed-train, score the pool by uncertainty plus diversity, batch to annotators, retrain, repeat. RED FLAG: picking only the most uncertain images and getting redundant near-duplicates.
What does N-way K-shot classification mean?
WHAT IT TESTS: few-shot evaluation vocabulary. OUTLINE: N is classes per episode, K is labeled examples per class in the support set, prediction is on a separate query set. RED FLAG: confusing K with total training data or swapping N and K.
Filter-based vs optimization-based SLAM
WHAT IT TESTS: SLAM estimation paradigms. OUTLINE: EKF folds past poses into one Gaussian; optimization keeps a sparse graph and re-linearizes; the latter wins on accuracy and loop closure. RED FLAG: thinking filters are more accurate because recursive.
Design real-time multi-object tracking for AV
WHAT IT TESTS: tracking-by-detection under latency. OUTLINE: fast detector, Kalman motion model, Hungarian association on IoU plus appearance, track lifecycle for occlusions. RED FLAG: per-frame detection with no temporal state or ID management.
Loss functions for imbalanced medical segmentation
WHAT IT TESTS: imbalanced segmentation losses. OUTLINE: cross-entropy is swamped by background; Dice optimizes overlap directly; Focal down-weights easy pixels. RED FLAG: only tuning class weights and ignoring the gradient problem.
How is IoU computed and why prefer mIoU?
WHAT IT TESTS: segmentation metrics under imbalance. OUTLINE: IoU is intersection over union of predicted and true pixels; mIoU averages per class; pixel accuracy is dominated by background. RED FLAG: equating accuracy with IoU.
Homography vs fundamental matrix degeneracy
WHAT IT TESTS: epipolar geometry degeneracies. OUTLINE: homography fits planar scenes or pure rotation; fitting a fundamental matrix there is degenerate because points lack depth variation. RED FLAG: assuming the fundamental matrix always works.
PTQ versus QAT for model quantization
WHAT IT TESTS: quantization workflows. OUTLINE: PTQ quantizes a trained model with calibration data, fast but may lose accuracy; QAT simulates quantization in training so weights adapt. RED FLAG: thinking QAT needs no data or PTQ is always lossless.
Why averaging shots reduces image noise
WHAT IT TESTS: noise statistics in imaging. OUTLINE: random noise averages out while signal stays, so SNR rises with the square root of frame count; limits include motion and fixed-pattern noise. RED FLAG: claiming noise drops linearly with frames.
The role of anchor boxes in detectors
WHAT IT TESTS: anchor-based detection. OUTLINE: anchors are reference boxes of set scales and ratios; the model regresses offsets, framing detection as classification plus refinement; downsides are tuning, imbalance. RED FLAG: anchors are not final boxes.
Triangulating 3D points from two views
WHAT IT TESTS: 3D reconstruction geometry. OUTLINE: back-project each 2D point into a ray, intersect rays from both cameras using known poses, solve via DLT and refine by minimizing reprojection error. RED FLAG: ignoring noise so rays never exactly meet.
Single vs multi-image super-resolution
WHAT IT TESTS: super-resolution methods and losses. OUTLINE: SISR hallucinates detail from one image, multi-image fuses sub-pixel shifts; pixel losses blur, perceptual or GAN losses add realism. RED FLAG: thinking L2 loss gives the sharpest result.
Non-maximum suppression in detection
WHAT IT TESTS: detector post-processing. OUTLINE: detectors emit many overlapping boxes per object; NMS keeps the highest-scoring box and removes others above an IoU threshold. RED FLAG: forgetting NMS is per-class or misstating why duplicates arise.
One-stage vs two-stage detectors
WHAT IT TESTS: detector architecture trade-offs. OUTLINE: one-stage predicts boxes directly for speed; two-stage proposes then refines for accuracy; focal loss narrows the gap. RED FLAG: claiming one is always better without the speed-accuracy framing.
Designing a multi-object tracker
WHAT IT TESTS: tracking-by-detection design. OUTLINE: detect per frame, predict motion with a filter, associate via IoU and appearance, manage track lifecycle, handle occlusion with re-ID. RED FLAG: ignoring data association or ID switches.
Challenges deploying a model on edge hardware
WHAT IT TESTS: edge deployment constraints. OUTLINE: limited memory and compute cause latency, thermal and power limits, accuracy loss from compression, operator support gaps. RED FLAG: only mentioning accuracy while ignoring latency, memory, and power.
When a homography is a valid model
WHAT IT TESTS: projective geometry assumptions. OUTLINE: homography holds for pure rotation or a planar scene; it fails with translation plus 3D parallax, where epipolar geometry applies. RED FLAG: thinking homography works for any two views.
Contrastive learning vs masked image modeling
WHAT IT TESTS: self-supervised pretraining. OUTLINE: contrastive aligns augmented views via instance discrimination; MAE reconstructs masked patches; they differ in augmentation and fine-tuning. RED FLAG: conflating them or saying one always wins.
Zero-shot classification with CLIP
WHAT IT TESTS: contrastive vision-language inference. OUTLINE: encode image and label prompts into a shared space, compare via cosine similarity, pick the highest. RED FLAG: thinking CLIP needs fine-tuning or a fixed label head to classify.
Point cloud vs voxel grid vs NeRF
WHAT IT TESTS: choosing 3D scene representations. OUTLINE: point clouds are sparse and fast but unstructured, voxels are regular for collision checks but memory-heavy, NeRFs render photorealistically but are slow. RED FLAG: declaring one universally best.