Intermediate everything in Computer Vision, page 2
Cross-attention in transformer VQA models
Text queries attend over image regions, learning alignment that grounds words to visual content.
The data association problem in SLAM
Matching observations to landmarks, why wrong matches corrupt the map, robust techniques like RANSAC and descriptor matching.
Core components of visual SLAM
Tracking estimates per-frame pose, mapping builds and refines the 3D map, loop closure detects revisits and corrects drift.
Adapting a 2D CNN for video action recognition
Run the 2D CNN per frame, pool features over time, optionally add two-stream or 3D conv.
Deploying segmentation on edge devices
Pick efficient architectures, apply INT8 quantization, distill from a large teacher.
Improving small object detection
Raise input resolution and tile, use feature pyramids for high-res features, tune anchors and copy-paste augmentation.
What is Bundle Adjustment and why is it tractable?
Jointly refine 3D points and camera poses by minimizing reprojection error, expensive due to many coupled parameters; sparsity of the Jacobian and the Schur complement make it tractable.
Why learn detection and description jointly like SuperPoint?
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.
A detector finds where interesting points are, a descriptor encodes the local appearance around each so points can be matched.
How is an HDR radiance map constructed from exposures?
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.
CPU vs GPU vs Edge TPU for inference.
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?
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?
Quantization, pruning, distillation, lighter backbones, and resolution or batching tweaks, each trading some accuracy or effort for speed.
Compare Gray World and White Patch white balance.
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?
Estimate per-pixel depth via dual-pixel or stereo or learning, segment the subject, then apply depth-dependent blur.
Prototypical Networks for few-shot classification
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.
Contrastive self-supervised learning with SimCLR
Two augmentations of one image form a positive pair, other images in the batch are negatives; an encoder plus projection head and the NT-Xent loss pull positives together and push negatives apart.
Camera intrinsics, extrinsics, and the essential matrix
Intrinsics map camera coords to pixels, extrinsics are camera pose in the world; the essential matrix relates normalized points across two views, encoding relative rotation and translation up to scale…
Unpaired image translation with CycleGAN
CycleGAN uses two generators and two discriminators with a cycle-consistency loss that forces translating to the other domain and back to reconstruct the input, removing the need for paired data.
How text prompts guide Stable Diffusion
A frozen text encoder turns the prompt into token embeddings, which feed the U-Net via cross-attention at each denoising step so the prompt steers generation; classifier-free guidance amplifies the…
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