Intermediate everything in AI & ML, page 9
3D CNNs vs two-stream action recognition
3D CNNs learn spatiotemporal filters end to end but are heavy; two-stream splits RGB appearance and precomputed optical flow, strong but costly to compute flow.
Kalman filter for bounding-box tracking
State, transition, measurement models, and process plus measurement noise; predict then correct each frame. State holds box position and velocity; measurement is the detected box.
Brightness constancy and small-motion assumptions
Brightness constancy says a point's intensity is invariant under motion; small motion lets you linearize via Taylor expansion.
How to improve coarse segmentation boundaries?
Skip connections and higher-resolution features, boundary-aware losses, and point-based or CRF refinement.
How does Mask R-CNN do instance segmentation?
Faster R-CNN backbone plus RPN, then RoIAlign and a parallel mask head predicting per-class binary masks.
U-Net architecture and its skip connections
U-Net has a contracting encoder, an expanding decoder, and skip connections that concatenate matching-resolution encoder features into the decoder to recover spatial detail lost in downsampling.
Uses of the 1x1 convolution
A 1x1 conv is a per-pixel linear combination across channels; it reshapes channel depth cheaply and adds nonlinearity. Uses: dimensionality reduction in bottlenecks and channel mixing.
Receptive fields in convolutional networks
Receptive field is the input region affecting a neuron; it grows with depth, larger kernels, and stride. It matters for capturing context in detection and segmentation.
ResNet residual blocks and the degradation problem
A residual block learns F(x) and adds the identity input x, so layers fit a residual; this eases gradient flow and solves the degradation problem.
Regularization techniques for an overfitting CNN
Data augmentation expands the effective dataset, dropout prevents co-adaptation, weight decay penalizes large weights, plus early stopping and batchnorm.
The PnP problem in Structure from Motion
PnP recovers a camera's pose from known 3D points and their 2D projections; it registers new frames against the existing point cloud in SfM.
Incremental Structure from Motion pipeline
Detect and match features, estimate two-view geometry, triangulate, then incrementally add images with PnP and refine via bundle adjustment.
Fundamental matrix versus essential matrix
Both relate corresponding points across two views via the epipolar constraint; the fundamental matrix works in pixel coordinates, the essential matrix in calibrated coordinates and encodes relative pose.
Bag of Visual Words model
Cluster many local descriptors (e.g. k-means) into visual words; assign each image's features to words; represent the image as a histogram of word counts for a classifier.
Descriptor matching and Lowe's ratio test
Match by nearest-neighbor descriptor distance; Lowe's ratio test keeps a match only if the best is clearly better than the second-best, rejecting ambiguous ones.
SIFT versus SURF versus ORB
SIFT is most accurate but slow with float descriptors; SURF approximates SIFT for speed; ORB is fast, binary, and free, ideal for real-time and embedded.
SIFT scale and rotation invariance
Scale-space extrema via difference-of-Gaussians give scale invariance; a dominant gradient orientation gives rotation invariance; the descriptor is a normalized gradient histogram.
Removing salt-and-pepper noise
Use a median filter; it replaces a pixel with the neighborhood median so extreme outliers are discarded.
Lens distortion and camera calibration
Radial distortion bends straight lines (barrel/pincushion), tangential comes from lens-sensor misalignment; calibrate with a known pattern to estimate intrinsics and distortion coefficients.
Zero-Shot, Few-Shot, and Chain-of-Thought Trade-offs
Zero-shot is cheap but weak on reasoning, few-shot adds demos at token cost, CoT boosts multi-step accuracy but spends the most tokens and latency.
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