Advanced interview questions in Computer Vision, page 2
Attention in diffusion U-Nets for text conditioning
Self-attention mixes spatial features at low-res blocks; cross-attention has image queries attend to text-token keys/values; placed inside transformer blocks.
Classifier-free guidance in diffusion models
Train one model jointly on conditional and dropped-condition inputs; at inference extrapolate from unconditional toward conditional prediction via a guidance scale, sharpening prompt adherence…
DDPM versus DDIM sampling trade-offs
DDPM is a stochastic Markov chain needing many steps; DDIM is a non-Markovian, deterministic sampler that skips steps for far faster inference and reproducible, invertible latents, trading a…
Designing a high-resolution photorealistic face generator
Weigh StyleGAN's fast, controllable style-based synthesis against diffusion's diversity and stable training; handle scale via progressive or multi-resolution synthesis; protect diversity to avoid mode…
NeRF limitations and advances for robotics
Original NeRF is slow to train and render, per-scene, static, and needs many calibrated views; address speed with explicit grids or Gaussian splatting, dynamics with time-conditioned fields, and scale with…
Formulating a multi-step robot manipulation task
Perception detects and localizes the mug, action space spans navigation and manipulation, and a reward shaped over subgoals (reach, grasp, transport, place) with sparse final success guides learning.
How does MAML's inner and outer loop work?
Inner loop does task-specific gradient steps from shared init, outer loop updates the init for fast adaptability via second-order gradients.
How do BYOL and Barlow Twins avoid representation collapse?
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 a plenoptic camera enable post-capture refocus?
Light field records ray direction plus position via a microlens array, refocusing shears and integrates the 4D data to choose a virtual focal plane.
Why merge a burst instead of one long low-light exposure?
Short frames avoid motion blur and clipping while averaging cuts noise; align frames and merge robustly, rejecting misaligned regions to avoid ghosting.
How does focus stacking build an all-in-focus image?
Align frames, measure local sharpness as a focus metric, select or blend the sharpest source per region, then composite.
Design a cashierless retail checkout vision system.
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.
Design drift detection and retraining for a CV model.
Monitor input and prediction distributions plus delayed labels, detect data and concept drift, trigger an automated retrain-validate-deploy pipeline with versioning and rollback.
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